Object-oriented machine learning governance

ABSTRACT

Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent claims the benefit of U.S. Provisional Patent Application62/856,713, filed 3 Jun. 2019, titled OBJECT-ORIENTED AI MODELING. Theentire content of each aforementioned application is hereby incorporatedby reference.

BACKGROUND 1. Field

The present disclosure relates generally to predictive computer modelsand, more specifically, to the creation and operation of numerousmachine learning (and other types of artificial intelligence) models ina form that is modular, reusable, easier to maintain, faster todevelopment, easily adapted and modified by data scientists and analystsalike.

2. Description of the Related Art

Data scientists today often struggle to manage predictive models thatnumber in the dozens or hundreds. This is the case because of theever-increasing appetite for predictive analytics, fueled by theexistence of open source tools and libraries. Such computer models areoften created or trained on different datasets, by those seeking toprovide support and insights for different objectives. Some companiesrun tens of thousands of propensity models every month just to keep up.

Not only is the development of machine learning models time consuming,it creates a legacy of technology debt, and the constant drive toimprove performance yields to the processing of the datasets isinconsistent from application to applications. The choice of featuremanagement, labeling, selection of positive and negative machinelearning classes, decision on which data is duplicated or redundant,which should be abstracted or aggregated varies from implementation toimplementation.

Existing computer systems for managing machine-learning models aretypically ill-suited for the diversity of contemporary models. In a widerange of use cases, machine learning models are used to predict hownon-deterministic entities or complex entities with emergent propertieswill behave under specific circumstances. Machine learning has taken abespoke and monolithic approach at times, confounding the selection ofan algorithm tweaked to situation at end (a dataset and a businessobjective) as “modelling” rather than creating a functional andadaptable pipeline leveraging subject matter expertise for features,workflows, and ontologies. While the world of software design has movedto microservices, refactoring large applications into independentlydeployable services allowing scaling of releases and more complexintegration, the machine learning world has been left behind, relyingprimarily on notebooks and single purpose/single flow models.

Indeed, prior manual approaches leave much to be desired. Merelyautomating such approaches fails to arm developers with tools needed foremerging levels of complexity in this field. Manual approaches oftenfail to reveal and account for higher-level classifications offunctionality that will be useful for managing complexity inmachine-learning deployments and reasoning about related code and modelsimplemented by that code. As a result, such manual approaches, even ifautomated, often fail to surface modularity in a corpus of models andsufficiently facilitate re-use of and adaptation of models in relateduse cases.

SUMMARY

The following is a non-exhaustive listing of some aspects of the presenttechniques. These and other aspects are described in the followingdisclosure.

Some aspects include a process, including: writing, with a computingsystem, a first plurality of classes using object-oriented modelling ofmodelling methods; writing, with the computing system, a secondplurality of classes using object-oriented modelling of governance;scanning, with the computing system, a set of libraries collectivelycontaining both modelling object classes among the first plurality ofclasses and governance classes among the second plurality of classes todetermine class definition information; using, with the computingsystem, at least some of the class definition information to produceobject manipulation functions, wherein the object manipulation functionsallow a governance system to access methods and attributes of classesamong first plurality of classes or the second plurality of classes tomanipulate objects of at least some of the modelling object classes; andusing at least some of the class definition information to effectuateaccess to the object manipulation functions.

Some aspects include a process, including encoding a quality managementprocess within model objects, where the encoded quality managementprocess expands between data quality to model quality, score(performance), label (ontology governance), or bias.

Some aspects include a process of performing governance managementprocesses embedded with components (modules) of modeling pipelines. Thisprocess may be used in model design, protection of PersonallyIdentifiable Information (PII) audit, modification, certification,development, validation, testing, deployment, upgrade, and retirement.

Some aspects include a process of decoupling the development of modelingtechniques from the characteristics of the datasets by creating theequivalent of patterns at multiple functional locations.

Some aspects include a process of decoupling labeling techniques fromthe labels themselves, expanding the nature and use of labels usingclasses of patterns and design patterns.

Some aspects include the decomposition of the modeling into racks ofoptions of modeling steps and optimizing the selection of said optionsthrough a constrained or unconstrained set of objectives.

Some aspects include the use of machine learning techniques andoperational research techniques to reduce a search space.

Some aspects include processes to create sequences of actions thatoptimize in the aggregate and in the individual over pipelineperformance.

Some aspects include a process of decoupling using message passing,decomposition of processing of data, and model transformation intoorganized collections of directed source-to-target mappings andpublish/subscribe paradigm.

Some aspects may apply to a variety of use cases. A use case may predictwhether a consumer is likely to make a purchase and determine whether tocause an advertisement to be conveyed to the consumer, e.g., whether tocause some form of message to be conveyed to the consumer via email,text message, phone call, mailer, or the like, or a discount should beoffered to the consumer. Another use case may predict whether a consumeris likely to submit a claim under a warranty and determine whether thatconsumer is qualified to be offered a warranty or price of the warranty.Another use case may predict whether the consumer is likely to pay offdebt and determine whether the consumer should be offered a loan orcredit card and terms, like interest rate or amount that can beborrowed. Another use case may predict whether a person is likely tobecome ill and determine whether that person should be offered insuranceor terms of the insurance, like deductible or maximum coverage. Anotheruse case may predict whether an industrial process, like an oilrefinery, plastic manufacturing plant, or pharmaceutical manufacturingplant, is likely to operate out of tolerance and determine whetherpreventative maintenance is warranted.

Some aspects include a tangible, non-transitory, machine-readable mediumstoring instructions that when executed by a data processing apparatuscause the data processing apparatus to perform operations including theabove-mentioned process.

Some aspects include a system, including: one or more processors; andmemory storing instructions that when executed by the processors causethe processors to effectuate operations of the above-mentioned process.

Some aspects include an implementation of the above with a distributedgeneral-purpose cluster-computing framework, such as Apache Spark,Apache Storm, Apache Hadoop, Apache Flink, Apache hive, Splunk, amazonKinesis, SQL stream, or Elasticsearch.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned aspects and other aspects of the present techniqueswill be better understood when the present application is read in viewof the following figures in which like numbers indicate similar oridentical elements:

FIG. 1 is a block logical and physical architecture diagram showing anembodiment of a controller in accordance with some of the presenttechniques;

FIG. 2 is a block diagram showing an embodiment of an object-orientationorchestrator in accordance with some of the present techniques;

FIG. 3 is a block diagram showing an embodiment of a modeling pillar inaccordance with some of the present techniques;

FIG. 4 is a block diagram showing an embodiment of a data orchestratorrack in accordance with some of the present techniques;

FIG. 5 is a block diagram showing an embodiment of an orchestrationsystem in accordance with some of the present techniques;

FIG. 6 is a flowchart showing an example of a pillar orchestration inaccordance with some of the present techniques;

FIG. 7 is a block diagram showing an embodiment of an OOM classes inaccordance with some of the present techniques;

FIG. 8 is a flowchart showing an example of the concept ofcontextualization and validation in accordance with some of the presenttechniques;

FIG. 9 is a block diagram showing an embodiment of a model object inaccordance with some of the present techniques;

FIG. 10 is a block diagram showing an embodiment of a modelor collectionused to create an optimized pipeline in accordance with some of thepresent techniques;

FIG. 11 is a block diagram showing an embodiment of an optimized modelorcollection in accordance with some of the present techniques;

FIG. 12 is a block diagram showing an embodiment of creation of anintegrated source to target mappings used in an orchestration inaccordance with some of the present techniques;

FIG. 13 is a flowchart showing an example of a process by which atargeted action is determined using an object-orientation orchestrator;

FIG. 14 is a flowchart showing an example of a process by which atargeted action is determined using an optimization system operating anobject-orientation orchestrator;

FIG. 15 is a flowchart showing an example of a process by which atargeted action is determined using a compiler function operating anobject-orientation orchestrator;

FIG. 16 is a flowchart showing an example of a process by which atargeted action is determined using a quality management systemoperating in an object-orientation orchestrator;

FIG. 17 is a flowchart showing an example of a process by which atargeted action is determined using an object-orientation orchestratorbased on a set of governance attributes; and

FIG. 18 shows an example of a computing device by which theabove-described techniques may be implemented.

While the present techniques are susceptible to various modificationsand alternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit thepresent techniques to the particular form disclosed, but to thecontrary, the intention is to cover all modifications, equivalents, andalternatives falling within the spirit and scope of the presenttechniques as defined by the appended claims.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

To mitigate the problems described herein, the inventors had to bothinvent solutions and, in some cases just as importantly, recognizeproblems overlooked (or not yet foreseen) by others in the fields ofcomputer science and data science. Indeed, the inventors wish toemphasize the difficulty of recognizing those problems that are nascentand will become much more apparent in the future should trends inindustry continue as the inventors expect. Further, because multipleproblems are addressed, it should be understood that some embodimentsare problem-specific, and not all embodiments address every problem withtraditional systems described herein or provide every benefit describedherein. That said, improvements that solve various permutations of theseproblems are described below.

FIG. 1 is a schematic block diagram of an example of a controller 10,operating within a computing system 100, in which the present techniquesmay be implemented. In some embodiments, the computing system 100 mayinclude an entity log repository 12, which in some cases may includeentity events 14 and entity attributes 16. Entity events may includetargeted actions, non-targeted actions, or both. The computing system100 may further include a targeted action repository 18 and a pluralityof potential targeted actions 20.

In some embodiments, the entity logs may be in the form of datasets. Insome of the embodiments, there may be four types of datasets: trainingdatasets, validation datasets, test (e.g., quality assurance) datasets,and application (or other types of target) datasets. A training datasetmay be a dataset used to fit parameters of a model or to find patternsin the dataset. Training datasets may include pairs of an input vectorand desired output targets. Based on the result of the comparison andthe specific modelor object being used, the parameters of the modellingsteps resulting from the binding of the modelor with the dataset may beadjusted for the sake of tuning or optimization (e.g., adjustingparameters of a model to move the output of an objective function closerto, or all of the way to, a local or global maximum or minimum). Avalidation dataset may be a dataset of examples used to tune theparameters of a model (modelor object binding with the dataset). A testdataset may be a dataset independent of the training dataset thatexhibits the same, or similar, statistical and semantic properties asthe training dataset. An application dataset may be a datasetindependent of the training dataset used to put a model into production.In some embodiments, a dataset may be repeatedly split between trainingdataset and validation dataset for the purpose of cross-validation.Using association, datasets may be linked to create a datasetassociation. Another use of dataset association may be the associationof target datasets segmented based on attributes. In some embodiments,segmentation may be based on time and datasets may be representations ofperiods of a business, such as week 1 data, week 2 data, etc. Throughdataset association, comparison of performance of models and KeyPerformance Indicator (KPI) over time may be facilitated and may be usedto trigger a retraining regime (itself implemented as a polymorphism insome cases). In some embodiments, different training datasets may beused for different modeling steps on a pipeline. In some embodiments,different validation datasets may be used for different modeling stepson a pipeline.

In some embodiments, obtained raw data may encode, or serve as a basisfor generating, entity logs related to multiple different entities.Examples include records each describing a history of events associatedwith an individual respective entity, e.g., with a one-to-one mapping oflogs to entities or with shared logs. In some cases, these events areevents describing exogenous actions that impinge upon the entity, likemessages sent to the entity, news events, holidays, weather events,political events, changes in vendors to an industrial process, changesin set points to an industrial process, and the like. In some cases,these events describe endogenous actions performed by the entity, like apurchase, a warranty claim, an insurance claim, a default, a payment ofa debt, presenting with a health problem, and out-of-toleranceindustrial process metric, process yield, weather phenomenon, and thelike. In some embodiments, the events are labeled with some indicia ofsequence, like an indicium of time, for instance, with date stamps orother types of timestamps. In some embodiments, the event logs arerecords exported from a customer relationship management system, eachrecord pertaining to a different customer, and which may include theresult of various transformations on such records. In some embodiments,entity events may include targeted actions (e.g., a targeted outcome),non-targeted actions, or both. In some embodiments, the actions includethose described as being selected among in U.S. patent application Ser.No. 15/456,059, titled BUSINESS ARTIFICIAL INTELLIGENCE MANAGEMENTENGINE, the contents of which are hereby incorporated by reference.

In some embodiments, entity logs may further include non-eventattributes. The non-event attributes may include attributes of people,like psychometric or demographic attributes, like age, gender,geolocation of residence, geolocation of a workplace, income, number andage of children, whether they are married, and the like. In someembodiments, the non-event attributes may include attributes of adatacenter, for instance, a cooling capacity, an inventory of HVACequipment therein, a volumetric flow rate maximum for fans, and thelike. In some cases, such attributes may include values indicatingtransient responses to stimulus as well.

In some embodiments, a plurality of potential targeted actions 20 mayinclude business objectives or target states of other non-linear, highlycomplex systems, like some forms of industrial process controls.

In some embodiments, the state to which the controller is responsive(e.g., in online use cases for publishers and subscribers) may beingested in a subject-entity event stream 22. In some embodiments, thestream may be a real time stream, for instance, with data being suppliedas it is obtained (e.g., within less than 10 minutes, 10 seconds, 1second, or 500 milliseconds of being obtained) by, or in relation to,subject entities (e.g. subscribers), for instance, in queries sent asthe data is obtained to request a recommended responsive action in viewof the new information.

In some embodiments, a controller 10 may include a class-basedObject-Oriented Modeling (OOM) orchestrator 24 (which in someembodiments may be referred to with the trade name CEREBRI) built aroundthe concept of objects.

In some embodiments, OOM orchestrator 24 be based, in part, on the broadprinciples of Object-Oriented Programming (OOP). From the perspective ofOOP, there may be differences arising from the inclusion and use ofdatasets, adaptation for machine learning development usage lifecycles,pipelines, self-improvement of models, design through composition, andmultiple-purpose labeling (MUPL). In OOM, the application of the “code”to “data” may, at least in part, modify the code. That may not be thecase in OOP. Unlike OOP, the code structure for OOM may be in multipleprogramming languages.

In some embodiments, OOM orchestrator 24 may implement functionalitythat includes one or more of: abstraction, aggregation, arbitrator,association, accessor, optimization, auditor, binding, orchestration,composition, composition sheets, composition association, ConcurrentOntology Labelling Datastore (COLD), contextualization,cross-contextualization, dataset, dataset association, data streams,encapsulation, governance, inheritance, labelling, messaging, modelor,orchestration, policing, policors, object-oriented modeling (OMM),object-oriented quality management (OQM), object-publish-subscribemodeling (OPSM), pipelining, realization, targeting, and winnowing.

In some embodiments, OOM orchestrator 24 may have various types ofclasses, including: pillars, ontologies, modelor, models, datasets,labels, windows, and customer journeys. In some embodiments, thesetechniques may be implemented in conjunction with the predictive systemsdescribed in U.S. patent application Ser. Nos. 15/456,059; 16/151,136;62/740,858 and Ser. No. 16/127,933, the contents of which are herebyincorporated by reference, e.g., by leveraging the data models therein,providing outputs that serve as features thereof, or taking inputs fromthese systems to form input features of the techniques described herein.

In some embodiments related to OOM, a class may be a program-code orprogram-data template used to create objects.

One of the challenges in bringing machine learning and object-orientedconcept together may be managing vocabulary. In machine learning,features may be individual measurable properties or characteristic of aphenomenon being observed. Features may be at times referred to asattributes. In some embodiments, a feature may be referred as anindependent variable, a predictor variable, a covariate, a regressor, ora control variable.

In object-oriented design, attributes may be elements of an object orclass definition. In some embodiments, when a concept may have dualmeaning (ML for machine learning, OO for object Oriented), a prefix ofML or OO may be applied, accordingly. This convention applies, amongothers, to labels, classes, attributes, and methods whenever there isambiguity.

In some embodiments, an element of CEREBRI may be a rack class. A rackis a framework or a pattern for a canonic modeling step without theinstantiated of a data set and application to the dataset.

In some embodiments, an element of CEREBRI may be a modelor class. Amodelor is a framework or a pattern for a modeling step without theinstantiated of a data set and application to the dataset. It may bedecoupled from the modeling step itself. A modeling step may be createdby binding a dataset or a dataset association with a modelor. A modelingstep may be used to perform computation, transformation, or mapping on awhole or a part of a dataset. Modelors that aim to achieve the samefunctionality in a modeling pipeline may belong to the same rack.Modelors may be parametrized. In some embodiments, these parameters maybe attributes. In some embodiments, an attribute set of modelors may begovernance attributes that may be leveraged prospectively orretrospectively. An attribute may be a type of intellectual propertyrights of the modelor code, for example, the type of license of the code(e.g., open source or proprietary).

In some embodiments, an element of CEREBRI may be an orchestration. ACEREBRI orchestration may have two sub-domains: A data domain thattransforms data into objects, and an AI (artificial intelligence) domainthat transforms these objects into results. These results can be score,indexes, lists, ranked order lists among others. Each sub-domain in anOOM based machine learning solution requires a nominal set of steps tobe executed in sequence and/or in parallel.

In some embodiments, orchestration may be an organization of acollection of steps that perform a specific objective when executed by acomputing system. Orchestrations may operate on objects to create newobjects and thus may be recursive. In some embodiments, there are twotypes of orchestration in CEREBRI: modelor orchestrations and modelorchestrations.

In some embodiments, modelor orchestrations may perform manipulationsand computations based on modelor objects to create pipelines. Anorchestration bound to at least one dataset is an example of what isreferred to as a pipeline.

In some embodiments, an element of CEREBRI may be optimization. TheCEREBRI optimization may be akin to a compiler function that selectswhich modelors to be used and what parameters of these modelors shouldemployed. In some embodiments, one or more modelors may be chosen fromeach rack. In some embodiments, no modelor is chosen from at least someof the racks. The parameters of the modelors and the order of the racksmay be optimized for one or more objective functions based on theconstraints of a modelor governance. The time horizon, location horizon,and datasets horizon may be set. The organization of the modelors to beoptimized may be carried out through heuristics, operation research, ormachine learning itself.

In some embodiments, optimization may use one or more of a randomforest-based approach (e.g., an ensemble of models trained withclassification and regression trees (CART)), simulated annealing,reinforcement learning, genetic algorithm, gradient descent, andR-Learning. The optimization methods may include: Bayesian search,Boruta, and Lasso. In some embodiments, optimization methods may beconstrained through hard limitations, governance limitations, andregularization.

In some embodiments, a pipeline may be implemented in at least oneprogramming language. The underlying method used for the computations ormanipulations is an example of what is referred as a sheet. In someembodiments, sheets may be designed through a procedural programmingframework, a hyper-parameter optimization, database queries, scripting,stored procedure, Directed Asynchronous Graph (DAG) ApplicationProgramming Interface (API) calls, or Source to Target Mapping (STM). Insome embodiments, a sheet may be compiled into a single archive allowingruntime to efficiently deploy an entire pipeline (or a selectedportion), including its classes and their associated resources, in asingle request. In some embodiments, a modeling orchestration may beexecuted using an engine such as stream set, NiFi, pulsar, Kafka, Kafka,RabbitMQ, NATS, Firebase, Pusher, SignalR, Databricks, Socket.IO,OSIsoft Pi, or Heron.

In some embodiments, objects or data being orchestrated through sheetsmay be written in Scala. In some embodiments, objects or data beingorchestrated through sheets may be written in Java. The objects or thedata being orchestrated may be compiled into jar files and stored on aJava Virtual Machine (JVM). In some embodiments, a sheet (configurableand configured by data scientists) of a composition of the objects ordata may be stored in JSON (JavaScript object notation). In someembodiments, a pipeline may be written in Python, Scala, and SQL(structured query language). A sheet may describe the flow of data ormessages from origin objects to destination objects and may also definehow to access, mutate, map, validate, and bind the data, dataset, ormessages along the way. Embodiments may use a single source object torepresent the origin object, multiple processors to mutate data andobjects, and multiple destination stages to represent destinationobject. In some embodiments, embodiments may use an object that triggersa task when it receives a message. To process large volumes of data,embodiments may use multithreaded sheets or cluster-mode sheets.

In some embodiments, when a sheet is generated though a design processusing OOM, embodiments may create a new corresponding sheet for a targetplatform through inheritance.

In some embodiments, a sheet may be compiled into a single archiveallowing runtime to efficiently deploy an entire application, includingits classes and their associated resources, in a single request. In someembodiments, an orchestration may be executed with the aid of aninterpreter.

In some embodiments, a class of modelors may be an example of what isreferred to as pillars. Pillar classes may purport to support elementsof machine learning systems. Pillar classes may answer questions suchas:

-   -   a. Who will engage in an action,    -   b. Commitment or loyalty to a brand,    -   c. Commitment to spend,    -   d. Commitment to tenure (and counter churn away),    -   e. Timing propensity of engagement,    -   f. Affinity for choices of engagement (including location and        channel) from selections,    -   g. Affinity for choices of engagement for synthesized        selections,    -   h. Action to be set engagement,    -   i. Clustering of attributes or behaviors,    -   j. Classification, and    -   k. Product or service recommendations.

In some embodiments, pillars may use advanced modeling, operationresearch, optimization, statistical analysis, and data sciencetechniques (e.g., machine learning modeling techniques MLMTs) that maybe applied to datasets that have been processed through pipelines.Datasets may change throughout pipelines that may include growing andshrinking in sizes, and growing and shrinking in dimensionality.

In some embodiments, different MLMTs may be used, including: OrdinaryLeast Squares Regression (OLSR), Linear Regression, Logistic Regression,Stepwise Regression, Multivariate Adaptive Regression Splines (MARS),Locally Estimated Scatterplot Smoothing (LOESS), Instance-basedAlgorithms, k-Nearest Neighbor (KNN), Learning Vector Quantization(LVQ), Self-Organizing Map (SOM), Locally Weighted Learning (LWL),Regularization Algorithms, Ridge Regression, Least Absolute Shrinkageand Selection Operator (LASSO), Elastic Net, Least-Angle Regression(LARS), Decision Tree Algorithms, Classification and Regression Tree(CART), Iterative Dichotomizer 3 (ID3), C4.5 and C5.0 (differentversions of a powerful approach), Chi-squared Automatic InteractionDetection (CHAD)), Decision Stump, M5, Conditional Decision Trees, NaiveBayes, Gaussian Naive Bayes, Multinomial Naive Bayes, AveragedOne-Dependence Estimators (AODE), Bayesian Belief Network (BBN),Bayesian Network (BN), k-Means, k-Medians, Expectation Maximization(EM), Hierarchical Clustering, Association Rule Learning Algorithms,A-priori algorithm, Eclat algorithm, Artificial Neural NetworkAlgorithms, Perceptron, Back-Propagation, Hopfield Network, Radial BasisFunction Network (RBFN), Deep Learning Algorithms, ReinforcementLearning (RL), Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN),Convolutional Neural Network (CNN), Stacked Auto-Encoders,Dimensionality Reduction Algorithms, Principal Component Analysis (PCA),Principal Component Regression (PCR), Partial Least Squares Regression(PLSR), Multidimensional Scaling (MDS), Projection Pursuit, LinearDiscriminant Analysis (LDA), Mixture Discriminant Analysis (MDA),Quadratic Discriminant Analysis (QDA), Flexible Discriminant Analysis(FDA), Ensemble Algorithms, Boosting, Bootstrapped Aggregation(Bagging), AdaBoost, Stacked Generalization (blending), GradientBoosting Machines (GBM), Gradient Boosted Regression Trees (GBRT),Random Forest, Computational intelligence such as but not limited toevolutionary algorithms, PageRank based methods, Computer Vision (CV),Natural Language Processing (NLP), and Recommender Systems.

In some embodiments, an engagement in an action may be measured throughmonetary propensity techniques, such as the ones described in U.S.patent application Ser. No. 16/127,933, the contents of which are herebyincorporated by reference. The timing or distribution of timing in anaction may be measured through timing propensity techniques such as theones described U.S. Patent Application 62/847,274, the contents of whichare hereby incorporated by reference. The location affinity in an actionmay be measured through journey propensity techniques, such as the onesdescribed in U.S. Patent Application 62/844,338, the contents of whichare hereby incorporated by reference.

In some embodiments, OOM may improve model development, readability,data engineering transformations, applicability to multiple datasets,applicability to multiple contexts, reusability by reducing thedimensionality, and complexity of machine learning program or set ofprograms efficiently. OOM concepts may allow creation of specificinteractions between modelor objects that may create new modelor objectsfor scoring, actioning, listing, or recommending based on data collectedover a collection of items, period of time, geographic intent, orcategorical definitions. Datasets may be bound to modelors to createmodeling steps. Modeling steps may be applied to target datasets torealize predictions, support decisions, find structure in data, findpatterns, detect outliers, and classify items, cluster items, andoptimized objective functions, while maintaining explicit or implicitrestrictions, without always being explicitly programmed to perform thepurposed realizations.

In some embodiments. CEREBRI may use abstract classes. In someembodiments, an abstract class is a superclass (e.g., a parent class)that cannot be instantiated. Embodiments may need to instantiate one ofits child classes to create a new object. Abstract classes may have bothabstract and concrete objects. Abstract objects may contain only objectsignature, while concrete objects may declare a modelor object body aswell, in some embodiments.

In some embodiments, labelling includes adding tags to data. Augmentingdata with labels may make it more informative and more manageable. Oneuse of labelling in object-oriented modeling may be management oflabels, not as elements in a list or table, but as objects allowing acollection of tags to be used. As objects, OO-labels may be managed andorganized through enforced set of grammar and semantic rules. Oneattribute of an OO-label may be a user facing text for sake of userexperience (UX). In some embodiments, OO-labels may be used in a singleontology used for multiple customers, each with a ML-label for theirbusiness realizing a MUPL. OO-labels may encode meta-data about adataset, control information, and governance information among otherthings.

Using a single ontology for multiple customers in CEREBRI may haveadvantages, including the ability to have different representations ofinformation for different purposes. In some embodiments, this techniquemay be used to provide label text in different languages. That said,embodiments are not limited to systems that afford these advantages,which is not to suggest that any other described attributes arelimiting.

In some embodiments, an element of CEREBRI is the Concurrent OntologyLabelling Datastore (COLD) methodology implemented in code of CEREBRI.In CEREBRI OOM, OO-labels may be governed by an ontology (anorganization of information). Ontology in COLD may be domain specific(e.g., in telco or insurance) or domain independent (e.g., in marketingor interactive voice response (IVR)) COLD framework forsuperclass/subclass inheritance. In some embodiments, this approach maybe enforced through a labelling format to a specific grammar or syntaxinside OO-labels via attributes. In some embodiments, OO-labelling maymap a customer/consumer/user facing labels (the ML-label) to a genericontology through a feature of the OO-labels. COLD, in some cases, isexpected to facilitate efforts by data scientists and softwaredevelopers to have a “dual-ledger” view of labels, one internal to thepipeline sheet (that is implementation and coding) and one external forgovernance, quality, or user interface requirements.

In some of the embodiments related to machine learning, new features(which may increase dimension of datasets) may also be obtained from oldfeatures using a method known as feature engineering using knowledgeabout a problem to create new variables. In some embodiments, whilefeature engineering may be part of the application of machine learning,it may be difficult and expensive in terms of time, resource, and talentperspective. In some embodiments, CEREBRI may useobject-publish-subscribe modeling to convey improvements in featureengineering from one object to another.

In some embodiments related to machine learning, features that may notbe helpful in the performance of the models may be removed (which maydecrease in dimensionality of datasets); this is an example of what isreferred to as feature selection. Feature selection may be helpful interms of the required time, resource, and talent perspective. In someembodiments, CEREBRI may use object-publish-subscribe modeling to conveyimprovements in feature selection from one object to another.

In some embodiments, OOM may create a generic feature engineeringmethodology, through polymorphism or inheritance, to optimize on a basisof a business domain or a vertical market.

In some embodiments, polymorphism (i.e., one name in many forms) mayfacilitate reuse of objects (e.g., objects with the same name, or symbolby which they are represented) in use cases implicating differentfunctionality. In some embodiments, polymorphism may be used to declareseveral modelor objects with the same name until the objects aredifferent in certain characteristics, such as context or segment withina dataset. In some embodiments, polymorphism may be used to declareseveral model objects with the same name until the objects are differentin certain characteristics, such as context. By using a modeloroverriding feature, some embodiments may override modelor objects of aparent class from its child class.

In some embodiments, inheritance may facilitate extension of a classwith child classes that inherit attributes and methods of the parentclass. Inheritance may facilitate achieving modeling reusability. Achild class may override values and modelor objects of the parent class.A child class may also add new data and functionality to its parent orshield details of the parent class. Parent classes are also known assuper classes or base classes, while child classes are also known assubclasses or derived classes. Inheritance may extend components withoutany knowledge of the way in which a class was initially implemented, insome embodiments. Declaration options, such as Public and Private, maydictate which members of a superclass may be inherited, in someembodiments. Objects may relate to each other with either a “has a”,“uses a” or an “is a” relationship, wherein the later may be aninheritance relationship, in some embodiments.

In some embodiments, consistency of implementation for datasets may beachieved by using the same polymorphic structures on multiple modelobjects.

In some embodiments, targeting may refer to design or use of a modeloror an orchestration towards a specific goal, purpose, or intent.Targeting of a model may include the definition and availability of atarget dataset.

In some embodiments, datasets may be objects that represent collectionof related set of information composed of individual variables that maybe manipulated individually or collectively. The data inside a datasetmay include sets or collection of examples that may be factors, events,items, or journeys. A journey is a collection of events organized alongone or more timelines.

In some embodiments, an element of CEREBRI may beobject-publish-subscribe modeling (OPSM) framework. In some of theembodiments using OPSM, senders of messages (e.g., publisher objects) donot choose the messages to be sent directly to specific receivers (e.g.,subscriber objects). Publishers may categorize published messages intotopics (which may be objects themselves) without the knowledge of whichsubscriber objects, if any, there are. Messages may be generated basedon logic inside publisher objects. Similarly, subscriber object mayexpress interest in one or more classes and only receive messages thatare of interest, without the knowledge of which publishers, if any,there are. OPSM may be used for feature engineering. OPSM may also beused for introducing new data sources. In some embodiments, OPSM may beused to create an audit trail of performance by having OQM be thepublisher. OPSM may be used to leverage Source-to-Target Mapping (STM).STMs may include sets of data transformation instructions that determinehow to convert the structure and content of elements of datasets in thesource system to the structure and content needed in the target system.STMs may assist modelors in efforts to identify columns or keys in thesource system and point them to columns or keys in the target systems.Additionally, modelors may map data values in the source system to therange of values in the target system. OPSM may allow the processing ofdata in a batch mode, in a streaming mode, or in a combination thereof,in some embodiments.

Topics may be organized through ontologies allowing constructions ofsubtopics, in some embodiments.

In some embodiments, an element of CEREBRI is data-stream. Data streamsmay be a time-sequenced (e.g., time-stamped) set of messages that sharethe same source and the same destination. Data stream may beinstantiated by binding an element of a dataset with another element ofa dataset for the purpose of staging transformations, in someembodiments.

The topic in an OPSM may be a data stream.

In some embodiments, attributes may be in the form of properties,features, contexts, state-machine state, or components among others.

In some embodiments, an auditor may be a specific class that captureshistorical information about multiple aspects of an operation. In someembodiments, auditor may subscribe to the attribute history of keyobjects. Auditors may be used for governance management.

In some embodiments, quality Management (QM) in an object-orientedmodeling paradigm that may be implemented as a process that integratesraw data ingestion, manipulation, transformation, composition, andstorage for building artificial intelligence models. In legacy designs,quality management may include (and in some cases may be limited to)Extract, Transform, and Load (ETL) phases of effort and to the reportingof model performance (e.g., recall, precision, F1, etc.) from an end toend perspective as a quality.

Deposition of design process and operation of models, developed usingOOM into objects, may facilitate efforts to cause quality to be embeddedin objects. Quality may be attributes of objects. Modelor, boundmodelors, and pipelines may be managed through multiple lifecyclesrather than a single one. In some embodiments, Object-Oriented QM (OQM)may have six components:

-   -   a. Data quality monitoring (DQM): DQM measures, not necessarily        exclusively (which is not to suggest that other lists are        exclusive), new or missing data source (table) or data element,        counts, mull count and unique counts, and datatype changes. DQM        may be used to figure out which data sources are reliable.    -   b. Model quality monitoring (MQM): MQM may measure, not        necessarily exclusively (which is not to suggest that other        lists are exclusive), model-based metrics, such as F1,        precision, recall, etc., or data, and triggers retraining for        drift.    -   c. Score quality monitoring (SQM): SQM may perform model        hypothesis tests, including Welch's t-test (e.g., parametric        test for equal means) and the Mann-Whitney U-test (e.g.,        non-parametric test for equal distributions). SQM may also        compute lift tables, a decile table based on the predicted        probability of positive class membership, with the cumulative        distribution function of positive cases added in. The gain chart        is a plot of the cumulative distribution function of positive        cases may be included as a function of quantile.    -   d. Label quality monitoring (LQM): Labels may be categorical and        bound by semantic rules or ontologies. LQM may be used to        understand which data sources are leverageable and impactful.        LQM may be used for data debt management and enhancing        compositions for performance.    -   e. Bias quality monitoring (BQM): Bias is a systematic        distortion of the relationship between a variable, a data set,        and results. Three types of bias may be distinguished:        information bias, selection bias, and confounding, in some        embodiments.    -   f. Private quality monitoring (PQM): Privacy may cover        personally identifiable information and access of privileged        information.

In some embodiments, OQM attributes may include count, unique count,null count, mean, max, min, standard-deviation, median, missing datasource, new data source, missing data element, new data element,sparsity of reward, data type change, Accuracy, Precision, Recall, F1,ROC AUC, TPR, TNR, 1-FPR, 1-FNR, brier gain, 1-KS, lift statistic, CVArea under Curve, 1-CV turn on, CV plateau, 1-brier turn on, brierplateau, MAPk, TkCA, Coverage, entropy coverage, MAPk cohort, TkCApopulation, action percentage change, no action percentage change,action frequency rate, action recency rate, normalized action frequencyrate, normalized action recency rate, expected reward, direct method,inverse propensity method, doubly robust method, weighted doubly robust,sequential doubly robust, magic doubly robust, incremental responserate, net incremental revenue, Mann Whitney Wilcoxon's U test, decileanalysis test, effect size test, and economic efficiency.

In some embodiments, abstraction and encapsulation may be tied together.Abstraction is the development of classes, objects, types in terms oftheir interfaces and functionality, instead of their implementationdetails. Abstraction applies to a modelor, a modeling orchestration, apipeline, datasets, or some other focused representation of items.Encapsulation hides the details of implementation.

In some embodiments, modeling abstraction in CEREBRI may concealcomplexity from users and show them only the relevant information.Modeling abstraction may be used to manage complexity. Data scientistand operation research developers may use abstraction to decomposecomplex systems into smaller components. In some embodiments, if onewants to leverage a propensity pillar modelor object, one may not needto know about its internal working and may just need to know the valueand context for usage. In some embodiments, if a modelor object wants tofactor in a sentiment modeling, embodiments may not need to know aboutits internal natural language processing working, and may just need toknow the value and context for usage in other modelor objects. The samemight be true of CEREBRI classes. Embodiments may hide internalimplementation details by using abstract classes or interfaces. On theabstract level, embodiments may only need to define the modelor objectsignatures (name, performance list, combination restriction,privacy-rules, and parameter list) and let each class implement themindependently.

In some embodiments, encapsulation in CEREBRI may protect the data andconfiguration stored in a class from system-wide access. Encapsulationmay safeguard the internal contents of a class like a real-life capsule.CEREBRI pillars may be implemented as examples of fully encapsulatedclasses. Encapsulation is the hiding of data and of methodsimplementation by restricting access to specific objects. Embodimentsmay implement encapsulation in CEREBRI by keeping the object attributesprivate and providing public access to accessors, mutators, validators,bindors, contextors, and policors to each attribute.

In some embodiments, accessors may be CEREBRI public objects used to askan object about itself. Accessor objects may be not restricted toproperties and may be any public modelor object that gives informationabout the state of the object. When an object is a model or modelor, itmay embed a machine learning state that is ML-State. The state of theobject may be to a large degree a superset the machine learning state ofa model that captures latent, Markov, reward, quality, or governanceinformation.

In some embodiments, mutators may be CEREBRI public objects used tomodify the state of an object, while hiding the implementation ofexactly how the modifications take place. Mutators may be suited forfeature engineering and for source to target mapping.

In some embodiments, contextors may be CEREBRI public objects used tomodify metadata, control, configuration, and data of a dataset object,while hiding the implementation of how the data gets modified. Acontextor may be used to reduce the range of event timing to considerdefining a positive ML-class. Contextors may be used tocross-contextualize datasets used in composition of model objects.

In some embodiments, bindors may be CEREBRI public objects used toassociate a specific dataset to a modelor. Bindors may be a specifictype of mutator. Binding may include the association of modelor object(developed as a single modelor object or the result of a composition) toa dataset or data-stream.

In some embodiments, arbitrators may be CEREBRI public objects used toreplace one object with another one from the same class. Arbitrators maybe used for automatic update of modelor, orchestrations, and pipelines.

In some embodiments, validators may be CEREBRI public objects ensuring,integrity, governance, and quality objects (e.g., quality managementobjects) used to ensure no data linkage or inconsistency of datasets,ML-labels, performance (OQM) and windows of processing. Validators maycheck database consistency applied to aspects of an OOM. A function ofvalidators may be triggering retraining of part or complete pipelinesbased on quality or operational triggers.

In some embodiments, governance in CEREBRI may be a set of structures,processes and policies by which pipeline development, deployment, anduse function within an organization or set of organizations is directed,managed, and controlled to yield business value and to mitigate risk.

In some embodiments, a policy in CEREBRI may refer to set of rules,controls, and resolutions put in place to dictate model behaviorindividually as a whole. Policies are a way governance is encoded andmanaged in some embodiments. The policy items are referred to aspolicors and implemented as CEREBRI objects. As the number of rulesincrease, a policy-driven OOM may suffer from inconsistencies incurredby contradicting rules governing its behavior. In some embodiments,meta-policies (e.g., detection rules) may be used for the detection ofconflicts. In some embodiments, policy relationships may be used. Insome embodiments, attribute-based applicability spaces may be used.

In some embodiments, contextualization in CEREBRI may refer torestriction of datasets to certain ranges in time, space, domain, objecttypes count, users, to accommodate the business and quality requirementof specific use cases. The contextualization may be affecting thedataset used to train (generate) the modelling steps (the one bound witha modelor). Contextualization may be affecting the dataset to which amodel is being applied.

In some embodiments, cross-contextualization in CEREBRI may beimplemented as a process to verify that the datasets used in models arequantitatively and qualitatively compatible and valid. A validity checkmay ensure (or reduce) no data linkage. A dataset used for training maynot be validated on itself or part of itself, in some embodiments. Adataset training may include information available at the moment of thetraining, not future data. Cross-contextualization may be performedthrough contextors, objects that compare datasets across differentpipelines and across different modeling steps.

Winnowing is the concept of limiting the scope or dimensionality of adataset. In some embodiments, winnowing may be achieved through ajudicious use of accessors and mutators. Some embodiments of winnowingmay be in the time domain (e.g., shortening a time range). Someembodiments of winnowing may be geography (e.g., reducing the geographicrange). Some embodiments of winnowing may be ontological (e.g., reducebranches and leaves of a taxonomy, reduce predicates insubject-predicate-object). Some embodiments of winnowing may be binningnumerical attributes into categorical attributes.

In some of embodiments, a publisher objects' subscription may be to aclass of objects or public attributes to the class. This capability ofOOM may be used for feature engineering. In some embodiments, the pillardealing with time optimization (e.g., Té) may be emphasized. Many KPIshave a timing element assigned to them (e.g., propensity to buy have atime dimension assigned to it). Churn may have an inherent timingdimension. Rather that optimizing separately, models for those KPIs maysubscribe to the Té engineered features. If the performance change,relearning will be trigged through validators. Another use of the OPSMis ensembling.

In some embodiments of inheritance, embodiments may implement amulti-channel or omnichannel marketing campaign. The campaign mayleverage to email, mails, in-store displays, or text message. At somelevel, all of these items may be treated the same: All four types mayinvolve creative, cost money to produce, have the same geographicalmarket area and lifetime. However, even though the types may be viewedas the same, they are not identical. An email has email address, a storedisplay does not. Each of these marketing campaign's assets should berepresented by its own class definition. Without inheritance though,each class must independently implement the characteristics that arecommon to channel assets. All assets may be either operational, ready todeploy, or deprecated. Rather than duplicate functionality, inheritanceis expected to facilitate re-use of functionality from another class.

Inheritance may be used to take a modeling step or pipeline and applyingit to a subset of the original dataset. In some embodiments, this isaccomplished by binding to a more restrictive dataset.

Inheritance may be used for feature engineering. In some embodiments,this is accomplished by defining broad features on the superclass andnarrower features in the child class.

In some embodiments, polymorphism may be used to repurpose a model inOOM. In some embodiments, an upsell model (e.g., pushing an existingcustomer to buy a more expensive version of an item she/he owns) may bedeveloped based on a modelor and dataset (one that is bound). Themodel's purpose may be for a business broad customer base. There may be,however, segments of customers within this base. They may include (1)customers who buy on a regular basis, (2) customers who are at risk, and(3) customers who own a specific item. At first glance, these customersmay be treated the same after all. They may have a name, account number,contact information, customer journey. All four types represent rightfultargets for the upsell activities. However, even though the three typesof customers may be viewed as the same, they are not identical becauseof the journeys. For maximum performance, each of these customersegments may be represented by its own class definition.

In some embodiments, CEREBRI may provide two ways to implementpolymorphism: object overloading (e.g., build-time polymorphism) andobject overriding (e.g., run-time polymorphism or dynamic polymorphism).Modelor object overloading happens when various modelor objects with thesame name are present in a class. When they are called, they may bedifferentiated by the number, order, context, and types of parameters.In some embodiments, the type of parameter in the object signature isindication of engineering feature. Modelor object overriding may occurwhen the child class overrides a modelor object of its parent.

Association may be implemented with the act of establishing arelationship between two unrelated classes. A specific type ofassociation is binding. Embodiments may perform association whendeclaring two fields of different types (e.g. car purchase and carservice) within the same class and making them interact with each other.The association may be a one-to-one, one-to-many, many-to-one, ormany-to-many relationship.

One of the associations is dataset association where embodiments mayestablish that multiple datasets are related and binding them to thesame sets of modelors.

Another example association is the association of a development pipelinewith a production pipeline. This may be used to accelerate thetranslation of a pipeline using one coding language for its sheet (e.g.Python) to another (e.g. Scala). This may be used to move from aninterpreted sheet to a compiled sheet.

In some embodiments, aggregation may be a kind of association. It mayoccur when there is a one-way “has a” relationship between the twoclasses associated through their objects. For example, every marketingmessage has a channel (email, mail, or text) but a channel does notnecessarily have a marketing message.

In some embodiments, OOM may cause resulting model objects to managetheir lifecycle autonomously by leveraging dataset association ormodelor association. This ability coupled with micro-services may helpwith operational resiliency.

In some of the embodiments, related to OOM, training may not be the samefor all elements of a pipeline.

In some embodiments, OOM may include the following:

-   -   a. A modeling object KOT performs feature engineering for models        dealing with reducing Churn,    -   b. A modeling object YOR performs KPI estimations for churn        based on model YR and the bound training dataset YMT and the        bound validation dataset YVT,    -   c. The modeling object YOR subscribes to the        time-feature-engineer topic for Churn,    -   d. A Governance object GOT subscribes to all topics,    -   e. The modeling objects KOT find that changing the training set        from YMT to YMT′ (that uses features that at least some of them        are different from YMT features) improves the performance of        models,    -   f. KOT uses OPSM to publish YMT′ as better training model for        churn models,    -   g. Governance object GOT uses polymorphism to create modelor YR′        from YR,    -   h. Modelor YR binds with YMT′ to create YOR′, and    -   i. GOT sends appropriate arbitrator to replace YOR with YOR′.

Another aspect of OOM is the ability of OOM modeling steps or entirepipelines objects to improve their performance semi-autonomously byleveraging association and OOPS. This may be accomplished through OQMassessing the impact of including a new set of data sources, and orfeature engineered attributes.

In some embodiments, OOM may include the following:

-   -   a. A modeling object SOT is performing feature engineering on        the time dimension. Feature engineering may include recency        feature engineering, frequency feature engineering, lag feature        engineering, difference feature engineering, and harmonic        analysis feature engineering, as described in U.S. Patent        Application 62/748,287, the contents of which are incorporated        by reference. Recency features may leverage the last time an        event took place. Lag feature engineering may be used        extensively by organizing timelines into periods. In difference        features engineering, the features are generated by creating the        difference of a given feature of any kind between two consequent        periods or subsequent periods,    -   b. Object SOT uses OPSM to publishes engineered features        definitions as a time-feature-engineer topic,    -   c. Modeling object TOR performs KPI estimations based on modelor        TR bound with training dataset ZMT and validation dataset ZMV,    -   d. Modeling object SOT creates new set of engineered features        FF′,    -   e. Modelor TR spawns modelor TR,    -   f. Modeling object TOR uses inheritance on ZMT to spawn dataset        ZMT′ that includes features FF′.    -   g. Modeling object TOR uses inheritance on ZMV to spawn dataset        ZMV′ that includes features FF′,    -   h. Modeling object TOR uses inheritance to modeling object TOR′,    -   i. Model object TOR binds modelor TOR′ with ZMT′, ZMV′, ZMA′ to        create modeling TR′,    -   j. Quality object QOR uses an accessor to gather the performance        of SQM for TR′ on validation data,    -   k. Quality object QOR compares performance (which can be a        variety of measurements) of SQM for TR′ and TR,    -   l. If the performance of TR′ is worse than performance of TR,        QOR messages to appropriate arbitrator to delete TR′, TOR′,        ZMT′, ZMV′, and    -   m. If the performance of TR′ is better than performance of TR,        QOR messages to appropriate arbitrator to replace ZOR with ZOR′,        ZR with ZR′, ZMT with ZMT′, ZMV with ZMV′.

FIG. 2 is a diagram that illustrates an exemplary architecture 2000 ofobject-oriented orchestration in accordance with some of the embodimentsof the present disclosure. Various portions of systems and methodsdescribed herein may include or be executed on one or more computersystems similar to orchestration system called object-orientatedorchestrator 2000. An object-orientated orchestrator may include twofunctional areas: a data orchestration module 2001 and an artificialintelligence (AI) orchestration module 2002.

In some embodiments, inside data orchestration module 2001, data may betransformed through process module 2003 into datasets with OO-labels.

In some embodiments, inside AI orchestration module 2002, the processingof objects may take place. Firstly, one or more pillars may be selectedbased on business needs (2004). Based on those choices, datasets may beprepared (as shown in block 2005) for use by the pillars. Anorchestration may be composed in module 2006 to create a modelingframework. This modelor may be then bound to one or more datasets inmodule 2007 through the process of binding to create one or morepipelines. In the next step, formed pipelines may be then evaluated inmodule 2008.

FIG. 3 is a diagram that illustrates an exemplary architecture 3000 ofobject-oriented pillar rack in accordance with some of the embodimentsof the present techniques. A scaled propensity modelor 3001 may be amodelor object of the probability of a consumer making an economiccommitment. This scaled propensity is an indicator of inherentcommitment of a customer to a service or product brand. When bound witha dataset, the result modeling object may be computed according tovarious techniques, such as the ones provided in U.S. patent applicationSer. No. 16/127,933, the contents of which are hereby incorporated byreference. A Té modelor 3002 may be used to calibrate the moments intime when specific consumer is likely to engage with specificactivities. The resulting models may be used for churn management ormarketing campaigns. Non-exhaustive examples of encoding are provided inU.S. Patent Application 62/748,287, the contents of which are herebyincorporated by reference. An affinity modelor 3003 may be employed tocapture ranked likes and dislikes of customers for specific items. Theseitems may be, among others, items, services, channels, agents, terms ofcontracts, banking and loans configurations. A best action modelor 3004may be used to create a framework for concurrent KPI compound bestactions at different points in customer journeys. A cluster modelingmodule 3005 may also be used to group customers based on behavior intofinite list for further processing.

FIG. 4 is a flow diagram that illustrates an exemplary OOMtransformation from data to data labeled (ML-label) in accordance withsome of the embodiments of the present techniques. A data transformationmay be done in a data orchestrator rack 4000. In some embodiments, thisrack may be a part of a data orchestration module 2001 shown in FIG. 2 .An ingestion rack 4001 may be the entry point for raw data. In thisrack, some of the following functions may take place: data and schemadrift may be controlled, file headers may be checked, version numbersmay be added to incoming files, data may be routed into clean/errorqueues, and data files may be archived in their raw format.

In some embodiments, a landing rack 4002 may cleanse a 1:1 copy of rawdata. In this rack, some of the following functions may take place:error records may be cleaned, column types may be changed from string tospecific data types, and a version number may be updated.

In some embodiments, a curation rack 4003 may standardize base rawtables. In this rack, some of the following functions may take place:incremental data may be processed, data normalization may be donethrough primary surrogate keys added, de-duplication, referentialintegrity may be checked, data quality may be checked (DQM) throughvalue thresholds and value format, client specific column names may beformed, and the version may be updated.

In some embodiments, a dimensional rack 4004 may manage an analyticaldata warehouse or data lake. In this rack, some of the followingfunctions may take place: data may be encoded in dimensional starschema, column names may be changed from user specific to domainspecific, extension tables as key value stores may be added for userspecific attributes, and the version number may be updated.

In some embodiments, a feature and label bank rack 4005 may extract andengineer features (ML-features) and labels (ML-labels). In this rack,some of the following functions may take place: data may be changed fromdimensional star schema to denormalized flat table, granularity of datamay be adjusted for events, customer-product pairs, and customers, andthe version number may be updated.

Data movements between racks may be controlled through a sheet. Such asheet may signal messages, data, or scripts element moving from racks tobackplane 4007 and messages, data, or scripts element going back toracks 4008.

FIG. 5 is a flow diagram that illustrates an exemplary OOM compositionof object-oriented pillars in accordance with some of the embodiments ofthe present techniques. An orchestration system 5000 may host a librarywith adjudication classes 5001, including:

-   -   a. Sequence: This class of mutators may change a collection of        items into a time sequences for processing.    -   b. Feature: This class may use accessors to gather one or more        ML-feature of a model or modelor, one or more of properties,        features, contexts, ML-state components, OO-state and then use        the features in another model or modelor object.    -   c. Economic optimization: This class may hold one or more        economic objectives and zero or more economic constraints        related to a unitary set of objects (e.g. a person, an product,        a service) or a finite set of unitary set of objects (e.g.        persons and products) or a finite set of unitary sets        complemented by geo-temporal domain (e.g. persons and products        and labor day in Maryland) and uses an allocation algorithm to        maximize the objectives. Examples of objective functions may        include margin optimization, revenue, number of items sold, and        carried interest. Examples of constraints may include Cerebri        Value range, cost of sales, and number of loan officers.        Examples of optimization techniques may include Evolutionary        algorithms, Genetic Algorithm (GA), simulated annealing, TABU        search, harmony search, stochastic hill climbing, particle swarm        optimization, linear programming, dynamic programming, integer        programming, stochastic programming, and shortest path analysis.    -   d. Horses for courses: This class may use accessors to gather        and then analyze different performance measures from the OQM        attributes of modelors and context thereof to select which        modelors out of the set of modelors to use for a specific set of        contexts based on maximize quality value computed from elements        of OQM. This class may also analyze different performance        measures from the OQM attributes of models and context thereof        to select which models out of the set of models to use for a        specific set of contexts based on maximize quality value        computed from elements of OQM.    -   e. Layering: This class may use accessors to gather and then        analyze different measures from the OQM attributes of modelors        and OO-features thereof organized along a semantically preset        taxonomy or ontology to select which performance measures may be        used per OOM-feature for use in a specific set of contexts. This        class may also analyze different measures from the OQM        attributes of models and OO-features thereof organized along a        semantically preset taxonomy or ontology to select which        performance measures should be used per OOM-feature for use in a        specific set of contexts.    -   f. Ensembling: This class may use accessors to gather and then        analyze the outputs and combine the decisions from multiple        models to improve the overall performance.    -   g. Publishing/subscribing: This class may use accessors to        gather relevant attributes and organize them according to        ontologies and mutators using those attributes.

In some embodiments, a pillar composition module 5002 may be used toleverage one or more pillars similar to the pillars shown in FIG. 3 andthe adjudication from module 5001 to develop a modelor (or if bound withdataset, model). Design analysis may determine that the most importantpillar is commitment and who is committed modelor 5003 may be invokedfirst. The second modelor to leverage may be timing module 5004. Thesequence composition 5005 may be used to connect the two. The affinitymodelor 5006 may be invoked next, triggered by message on economicoptimization 5007. The last pillar being invoked may be the how-tomodelor 5008. The final actions may be set in module 5010, messagedthrough module 5011. Modules may perform forward messaging or backwardmessaging to connect with non-adjacent modules. For example, module 5003may message module 5008 through 5012 (e.g., forward messaging) or module5006 may message module 5004 through 5013 (e.g., backward messaging).

FIG. 6 is a flow diagram that illustrates another exemplary pillarcomposition module 5002 for a dual target OOM orchestration ofobject-oriented pillars in accordance with some of the embodiments ofthe present techniques. This flow diagram illustrates the integratedmodeling flow that may facilitate additional improvements with reducedcomplexity that improve performance post original design. The pillarsmay have OQM analysis features, such as feature importance, incrementalcontribution, Shapley information (like Shapley values, or othermeasures of network centrality), Gini impurity, entropy, and crossentropy.

Objective 1 6000 may capture a first business or organizationalobjective. FIG. 6 provides examples related to a marketing campaign of anew product for the sake illustration. Communication between all objectsmay be through messaging. Objective 2 (in box 6001) may capture a secondbusiness or organizational objective. This might be related to an eventcampaign like a sell-athon for the sake of illustration. Design analysismay determine the first pillar to drive the design of Objective 1 iscommitment and who is committed modelor 6002 may be invoked first. Thesecond modelor to leverage may be timing module 6003. Module 6003 maypass messages to the affinity modelor 6004. The last pillar being usedmay be the how-to modelor 6005. There may be a similar flow to supportObjective 2. For example, the first modelor to leverage may be timing6006, messaging the commitment modelor 6007, itself messaging actionmodelor 6008, and the affinity modelor 6009.

The OQM module 6010 inside modelor 6003 may be used to assess thepotential for improvement by leveraging affinity data from ODM module6011 in module 6004 by messaging back and forth 6012.

In some embodiments, this technique may be applied across objectives aswell. The OQM module 6013 inside modelor 6008 may be used to assess thepotential for improvement by timing affinity data from ODM module 6016in module 6003 by messaging back and forth 6015.

Operation research selection of the best actions may take place inmodule 6016. Messages to this module may come from the deepest module inthe Objective 1 flow 6017 or the middle module in the Objective 2 flow6018.

In some embodiments, a governance object 6019 may determine who hasaccess to module 6009. This control may be used to limit access based onthe role of users or persona of a user and what product to sell, forinstance.

FIG. 7 illustrates examples of some of the CEREBRI OOM classes 50 inaccordance with some of the embodiments of the present disclosure.ML-labels are shown in the ML-label class library 7000. (Functionalitydescribed as implemented as libraries may also be implemented inframeworks.) A KPI class 7001 may be used to manage the businessproblems. Two business models may be subscription and purchase. Acustomer class 7002 may capture the business/lifecycle of customerswhether consumers (for B2C) or businesses (for B2B). They may include atrisk customers, or all customers. An item class 7003 may definecommercial items. These items may be physical goods (e.g., cars) orservices (e.g., wireless phone contracts). These items may be rankedhierarchically. That hierarchy or unstructured metadata may be setthrough classes, such as models, options, and customization. In someembodiments, the hierarchy may be a taxonomy hierarchy.

A pillar class library 7005 may include scaled propensity/Cerebri Value7006 (a term which is described in the applications incorporated byreference), timing class 7007, affinity class 7008, and compound bestaction class 7009.

In some embodiments, adjudication classes of modelors or model objectsmay be organized similar to the library shown in block 7010. Not allcompositions may apply to all pillars or KPIs. Modelor compositions andmodel object compositions may include:

-   -   a. Sequence: This class of mutators may change a collection of        items into a time sequences for processing.    -   b. Feature: This class may use accessors to gather one or more        ML-feature of a model or modelor, one or more of properties,        features, contexts, ML-state components, and OO-state and then        use the features in another model or modelor object.    -   c. Economic optimization: This class may hold one or more        economic objectives and zero or more economic constraints        related to a unitary set of objects (typically, but not limited        to, a person, an product, a service) or a finite set of unitary        set of objects (e.g., persons and products) or a finite set of        unitary sets complemented by geo-temporal domain (e.g., persons        and products and labor day in Maryland) and uses an allocation        algorithm to maximize said objectives. Examples of objective        functions include margin optimization, revenue, number of items        sold, and carried interest. Examples of constraints include        Cerebri Value range, cost of sales, and number of loan officers.        Examples of optimization techniques include Evolutionary        algorithms, Genetic Algorithm (GA), simulated annealing, TABU        search, harmony search, stochastic hill climbing, particle swarm        optimization, linear programming, dynamic programming, integer        programming, stochastic programming, and shortest path analysis.    -   d. Horse for courses: This class may use accessors to gather and        then analyze different performance measures from the OQM        attributes of modelors and context thereof to select which        modelors out of the set of modelors to use for a specific set of        contexts based on maximize quality value computed from elements        of OQM. This class may also analyze different performance        measures from the OQM attributes of models and context thereof        to select which models out of the set of models to use for a        specific set of contexts based on maximize quality value        computed from elements of OQM.    -   e. Layering: This class may use accessors to gather and then        analyze different measures from the OQM attributes of modelors        and OO-features thereof organized along a semantically preset        taxonomy or ontology to select which performance measures should        be used per OOM-feature for use in a specific set of contexts.        This class may also analyze different measures from the OQM        attributes of models and OO-features thereof organized along a        semantically preset taxonomy or ontology to select which        performance measures should be used per OOM-feature for use in a        specific set of contexts.    -   f. Ensembling: This class may use accessors to gather and then        analyze the outputs and combine the decisions from multiple        models to improve the overall performance.    -   g. Publishing/subscribing: This class may use accessors to        gather relevant attributes and organize them according to        ontologies and mutators using those attributes.

In some embodiments, modeling methodology classes 7011 may capture someof the key accessors and mutators. Contextualization classes 7012 mayinclude binning (e.g., mapping of continuous attributes into discreteones), winnowing (e.g., reduction of time span, location foci, andbranches in semantic tree), selection of data sources, and selection ofKPIS.

In some embodiments, biding classes 7013 may include binding (or othertype of association) of, for instance, the four types of datasets (e.g.,training, test, validation, and application). The governance classes(7014) may capture the restrictions and business protocols for specificKPIs. They may include OR criteria, operational criteria, actions thatare allowed, and action density (e.g., number of actions per unit time).

In some embodiments, deployment classes 7016 may include realizations7017 including Cerebri Values and numerous KPIs, organized as primaryand secondary. Deployment classes 7016 may also include qualitymeasurements 7018 including data quality monitoring (DQM), model qualitymonitoring (MQM), score quality monitoring (SQM), bias qualitymanagement (BQM), privacy quality management (PQM), and label qualitymonitoring (LQM). Deployment classes 7016 may also include governanceclasses 7019 including support of client model validation using modeldocumentation, CRUD management of items and their metadata, securitycontrol of governance decision maker, QM metric thresholds asconstraints for optimizer, data DQM metric threshold evaluation andanalysis, data DQM metric creep evaluation through data set detection,data lifecycle with gate points and workflow actions, model MQM metricthreshold evaluation and analysis, model MQM metric creep evaluationthrough model drift detection, model output SQM metric thresholdevaluation and analysis, and model lifecycle with gate points andworkflow actions.

FIG. 8 illustrates an example of contextualization and validationimplemented in accordance with some of the embodiments of the presenttechniques. An orchestrator may be processing a composition betweenmodel object modules 8000 which are bound with dataset association 8001and model object 8002 which are bound to dataset association 8003. Theintersection of these two datasets is dataset 8004. The compositionbetween 8000 and 8002 is 8005 and its composition rule are set validatorobject 8006. To ensure the composition is correct, a cross-contextor8007 may be applied to datasets 8002, 8003, and composition rule 8006. Across-contextualization may determine that this composition 8006requires the same dataset association to be used. A new model object8007 may be created by binding the modelor 8008 that is used to createmodel object 8000 with the dataset 8004. A new model object 8009 may becreated by binding the modelor 8010 that is used to create model object8002 with the dataset 8004. Model 8007 may be composed with model 8009using composition 8005 and then composition 8001 to create best actionmodel 8012.

FIG. 9 illustrates an example of a model object field in accordance withsome of the embodiments of the present techniques. An orchestrator 9000may hold the classes 9001 that may have multiple attributes. Attributesmay be divided into multiple buckets, such as object management andlifecycles 9002, governance 9003, and machine learning techniques 9004.

In some embodiments, a modelor collection 10000 may be used to create anoptimized pipeline, may include a series of racks corresponding tonominal steps of a machine learning pipeline as shown in FIG. 10 . Rack10001 may support modelor dealing with data imputation. Rack 10002 maysupport modelors dealing with outliers. Rack 10003 may support modelorsdealing with data augmentation. Rack 10004 may support modelors dealingwith feature enrichments. Rack 10005 may support modelors dealing withthe splitting of datasets. Rack 10006 may support modelors dealing withsampling and balancing datasets. Rack 10007 may support modelors dealingwith feature selections. Rack 10008 may support modelors that performmodeling activities. Rack 10009 may support modelors computingvalidation and scoring. Rack 10010 may support modelors dealing withadjudication heuristics and methodologies. Rack 10011 may supportmodelors implementing operation research. The first five racks may bemonitored by an “Engineering features for the feature label bank” module10013. The sampling 10006 and feature selection 10007 racks may bemonitored by a “creating model ready datasets” module 10014. A modelbuilding rack 10008 may be monitored by a “model building” module 10015.The rest of the racks (e.g. 10009, 10010, and 10011) may be monitored bya “deploying scoring/validation” module 10016. While some may shortcutthrough an optimizer 10017, some of the monitoring modules may passthrough an optimizer 10017 to reduce the search space for optimization.Some of the optimization techniques in 10017 may be nondeterministicpolynomial time (NP) hard. For those, the functional organization ofracks and embedded modelors may reduce the computational load. Agovernance module 10018 may manage the optimization module.

FIG. 11 illustrates an exemplary outcome of an optimization set up inFIG. 10 . A machine learning system 11000 governed by governance module11001 may be resulted into an optimizer 11002 selecting a directed graph11003.

FIG. 12 illustrates an exemplary graphic editor 12001 of a source totarget mapping capabilities facilitated by some of the presenttechniques. Using a graphic user interface, embodiments may facilitateselection of elements 12001 of a STM. The network of STM may be managedthrough workspace pane 12002. Tags needed for building elements may bein panel 12003. In a workspace panel, a user may design sources (12004),mappings (12005), targets (12007), and links 12007 of a directed graphthat may capture their orchestration. UI interactions may be received byevent handlers, which may launch corresponding routines that updateconfigurations (e.g., configuration files) by which the techniquesdescribed herein are directed.

In some embodiments, the OOM framework may be implemented as a libraryinstead or as a hybrid of a library and a framework. In someembodiments, OOM framework may be implemented in a purely-objectoriented programming language, or in a hybrid language with support formultiple paradigms, e.g., also supporting functional programming orimperative programming support. The OOM framework may be implemented asa compiled or as an interpreted language, e.g., for the latter, with aninterpreter that interprets source code to bytecode, which can becompiled to machine code and executed via a suitable virtual machine fora host computing system.

The OOM framework may organize database structure, program code, andprogram state in objects. Objects may include both methods (which mayalso be referred to as routines, procedures, or functions), andattributes, which may be data of the object. Objects may be instances ofclasses supported by the OOM framework, e.g., instance A of class 1 andinstance B of class 1 may both be objects with the same set of aplurality of methods and attributes, and after, or as part ofinstantiations, the values taken by the attributes may evolve and differbetween the object that is instance A and the object that is instance B,e.g., instance A and instance B may be independent from one another. Insome cases, classes may support class variables that are accessible toobjects that are instances of the respective class. In some cases,methods of objects may have access (e.g., to read, write, or both readand write) to attributes of the respective object, but not to attributesof other objects. In some cases, some attributes of some objects may bedesignated as public or private to modulate access by methods otherobjects. In some cases, objects may receive messages, like invocationsof their methods from other objects, and the called object may determinewhich code to execute in response to the invocation, e.g., with dynamicdispatch, and in some cases, different instances of a class may beconfigured to dispatch to different code for the same invocationmessage. In some cases, objects may be composed of other objects ofother classes, and in some cases, classes may indicate inheritance toorganize objects according to a hierarchical ontology of types, e.g.,where sub-types inherit the attributes and methods of the types fromwhich they inherit. In some cases, the OOM framework may supportpolymorphism, and objects may be configured to operate on a type ofobjects, its sub-types, or its sub-sub-types.

In some cases, designs in the OOM framework may be implemented withvarious design pattern. For example, the following creation patterns maybe used: Factory method pattern, Abstract factory pattern, Singletonpattern, Builder pattern, or Prototype pattern. In some cases, thefollowing structural patterns may be used: Adapter pattern, Bridgepattern, Composite pattern, Decorator pattern, Facade pattern, Flyweightpattern, or Proxy pattern. In some cases, the following behaviorpatterns may be used: Chain-of-responsibility pattern, Command pattern,Interpreter pattern, Iterator pattern, Mediator pattern, Mementopattern, Observer pattern, State pattern, Strategy pattern, Templatemethod pattern, or Visitor pattern.

In some cases, the OOM framework specifies a set of symbols to modelvarious aspects of creating and using machine-learning models. In somecases, the OOM framework specifies an ontology of classes (which may behierarchical), with different branches or hierarchies pertaining todifferent areas of concern in the life cycle of machine learning models,like those discussed above.

In some embodiments, the controller 10 (e.g., an object-orientedmodeling module thereof) may execute a process 200 shown in FIG. 13 . Insome embodiments, different subsets of this process 200 may be executedby the illustrated components of the controller 10, so those featuresare described herein concurrently. It should be emphasized, though, thatembodiments of the process 200 are not limited to implementations withthe architecture of FIG. 1 , and that the architecture of FIG. 1 mayexecute processes different from that described with reference to FIG.13 , none of which is to suggest that any other description herein islimiting.

In some embodiments, the process 200 (and other functionality herein)may be implemented with program code or other instructions stored on atangible, non-transitory, machine-readable medium, such that when theinstructions are executed by one or more processors (a term which asused herein refers to physical processors, e.g., implemented on asemiconductor device), the described functionality is effectuated. Insome embodiments, notwithstanding use of the singular term “medium,” themedium may be distributed, with different subsets of the instructionsstored on different computing devices that effectuate those differentsubsets, an arrangement consistent with use of the singular term“medium” along with monolithic applications on a single device. In someembodiments, the described operations may be executed in a differentorder, some or all of the operations may be executed multiple times,operations may be executed concurrently with one another or multipleinstances of the described process, additional operations may beinserted, operations may be omitted, operations may be executedserially, or the processes described may otherwise be varied, again noneof which is to suggest that any other description herein is limiting.

In some embodiments, the process 200 includes obtaining, as indicated byblock 202, for a plurality of entities, datasets. The datasets may beevents or attributes involving the entities. In some embodiments, atleast a subset of the events are actions by the entities and some ofthese actions may be targeted actions.

In some embodiments, a plurality of objects may be formed, as indicatedby block 204. A data domain may be used to transform datasets intoobjects. Objects may include events and attributes of entities(attributes of entities and events may be a type of attribute of anobject) extracted from datasets.

In some embodiments, these datasets may be labeled with object-orientedtags, as indicated by block 206, used to classify entity logs. In someembodiments, labelling is a process of adding tags to data. Augmentingdata with labels may make it more informative and more manageable. Oneuse of labelling in object-oriented modeling may be management oflabels, not as elements in a list or table, but as objects allowing acollection of tags to be used. As objects, OO-labels may be managed andorganized through enforced set of grammar and semantic rules. Oneattribute of an OO-label may be a user facing text for sake of userexperience (UX). In some embodiments, OO-labels may be used in a singleontology used for multiple customers, each with a ML-label for theirbusiness realizing a MUPL. OO-labels may encode meta-data about adataset, control information, and governance information among otherthings.

In some embodiments, a library of classes may be formed, as indicated byblock 208. The library of classes may include the classes shown in FIG.7 . Also, a plurality of object-manipulation functions may be formed asindicated by block 210. Each of the object-manipulation objects may beconfigured to leverage a specific class.

In some embodiments, an object-orientated orchestration may be formed asindicated by block 212. An orchestration may be an organization of acollection of steps that perform a specific objective when executed by acomputing system. Orchestrations may operate on objects to create newobjects and thus may be recursive.

In some embodiments, the object-orientated orchestration may then beused to select a set of actions to achieve a given targeted actionsimilar to the process shown in FIG. 2 . To this end, some embodimentsmay receive a request from an entity or a subscriber as indicated byblock 214. Some embodiments may then determine the set of actions toachieve (or increase the likelihood of achieving) the given targetedaction, as indicated by block 216 in FIG. 13 , for instance, with apotential targeted action 20 in FIG. 1 .

In some embodiments, the controller 10 may execute a process 300 shownin FIG. 14 . In some embodiments, different subsets of this process 300may be executed by the illustrated components of the controller 10, sothose features are described herein concurrently. It should beemphasized, though, that embodiments of the process 300 are not limitedto implementations with the architecture of FIG. 1 , and that thearchitecture of FIG. 1 may execute processes different from thatdescribed with reference to FIG. 14 , none of which is to suggest thatany other description herein is limiting.

In some embodiments, the process 300 includes obtaining, as indicated byblock 302, for a plurality of entities, datasets. The datasets may beevents or attributes involving the entities. In some embodiments, atleast a subset of the events are actions by the entities and some ofthese actions may be targeted actions. The actions may include exogenousactions and endogenous actions.

In some embodiments, a plurality of objects may be formed, as indicatedby block 304. In some embodiments, all of the datasets are transferredinto objects. In some embodiments, only a portion of the datasets aretransferred into objects. Objects may include events and attributesextracted from datasets, both of which may be attributes of therespective object.

In some embodiments, these datasets may be labeled with object-orientedtags, as indicated by block 306, used to classify entity logs. In someembodiments, labelling is a process of adding tags to data.

In some embodiments, a library of classes may be formed, as indicated byblock 308. The library of classes may include the classes shown in FIG.7 . Also, a plurality of object-manipulation functions may be formed asindicated by block 310. Each of the object-manipulation objects may beconfigured to leverage a specific class.

In some embodiments, an object-orientated orchestration may be formed asindicated by block 312. An orchestration may be an organization of acollection of steps that perform a specific objective when executed by acomputing system. Orchestrations may operate on objects to create newobjects and thus may be recursive.

In some embodiments, a request may be received from an entity (e.g.,another object or other body of executing code) or a subscriber asindicated by block 314 to determine a set of actions required to achievea specific targeted action specified by the request. Some embodimentsmay then determine the set of actions to achieve (a term used broadly toalso refer to increasing the likelihood of achieving) the given targetedaction, using a compiler function as indicated by block 316 in FIG. 14 ,for instance, with a potential targeted action 20 in FIG. 1 . A compilerfunction may first pick a set of classes based on the specific targetedaction and then use a series of object-manipulation functions todetermine the set of actions, needed to be done by the entity or otherentities, to achieve the specific targeted action.

In some embodiments, the controller 10 may execute a process 400 shownin FIG. 15 . In some embodiments, different subsets of this process 400may be executed by the illustrated components of the controller 10, sothose features are described herein concurrently. It should beemphasized, though, that embodiments of the process 400 are not limitedto implementations with the architecture of FIG. 1 , and that thearchitecture of FIG. 1 may execute processes different from thatdescribed with reference to FIG. 15 , none of which is to suggest thatany other description herein is limiting.

In some embodiments, the process 400 includes first obtaining, asindicated by block 402, for a plurality of entities, datasets.Thereafter, multiple objects may be formed, as indicated by block 404,and the datasets may be labeled with object-oriented tags, as indicatedby block 406, used to classify entity logs.

In some embodiments, a library of classes may be formed, as indicated byblock 408. The library of classes may include the classes shown in FIG.7 . Also, a plurality of object-manipulation functions may be formed asindicated by block 410. Each of the object-manipulation objects may beconfigured to leverage a specific class.

In some embodiments, a training dataset may be formed using the datasetsof the plurality of entities, as shown by block 412. In someembodiments, the training dataset may describe scenarios that haveoccurred in the past. In some embodiments, the training dataset maydescribe scenarios that have not occurred, and thus are virtual. Use ofthe terms “form” and “generate” are used broadly and use of differentterms should not be read to necessarily refer to different operations invirtue of using different terminology, as both of these terms generallyinclude causing the described thing to come into being, whether bymodifying an existing thing or forming a new copy or instance.

Next, some embodiments may input the training datasets into a machinelearning model model that indicates interdependency of the plurality ofobject-manipulation functions in leveraging a specific class, asindicated by block 414 in FIG. 15 . In some embodiments, the trainedmodel may, in response, output a score indicative of interdependency ofthe plurality of object-manipulation functions. In some cases, theoutput may be one of the scores or values described in patentapplication Ser. No. 15/456,059, titled BUSINESS ARTIFICIAL INTELLIGENCEMANAGEMENT ENGINE, the contents of which are hereby incorporated byreference.

In some of the embodiments related to machine learning, new features(which may increase dimension of datasets) may also be obtained from oldfeatures using a method known as feature engineering using knowledgeabout a problem to create new variables. In some embodiments, whilefeature engineering may be part of the application of machine learning,it may be difficult and expensive in terms of time, resource, and talentperspective. In some embodiments, CEREBRI may useobject-publish-subscribe modeling to convey improvements in featureengineering from one object to another.

In some embodiments related to machine learning, features that may notbe helpful in the performance of the models may be removed (which maydecrease in dimensionality of datasets); this is an example of what isreferred to as feature selection. Feature selection may be helpful interms of the required time, resource, and talent perspective. In someembodiments, CEREBRI may use object-publish-subscribe modeling to conveyimprovements in feature selection from one object to another.

In some embodiments, when an object is a model or modelor, it may embeda machine learning state that is ML-State. The state of the object maybe to a large degree a superset the machine learning state of a modelthat captures latent, Markov, reward, quality, or governanceinformation.

Different types of training may be applied depending upon the type ofmodel in use. Any of the types of models described above may be applied.In some embodiments, the model is policy in a reinforcement learningmodel. In some embodiments, the model is a classifier configured toclassify object classes. Various types of processing may be performed bymachine learning model on the datasets, including the processes shown inFIG. 10 .

In some embodiments, multiple interdependency graphs are formed, asshown by block 416, which can track the relationship between differentclasses.

Some embodiments may store the resulting model in memory, as indicatedby block 418. As noted, trained models may be expressed as a lookuptable mapping inputs to outputs, sets of values for constants orvariables in software routines, as values of parameters in closed-formequations, or combinations thereof.

In some embodiments, an object-orientated orchestration may be formed asindicated by block 420. An orchestration may be an organization of acollection of steps that perform a specific objective when executed by acomputing system.

In some embodiments, the object-orientated orchestration may then beused to select a set of actions to achieve a given targeted actionsimilar to the process shown in FIG. 2 . To this end, some embodimentsmay receive a request from an entity or a subscriber as indicated byblock 422. Some embodiments may then determine the set of actions toachieve (or increase the likelihood of achieving) the given targetedaction, as indicated by block 424 in FIG. 135 for instance, with apotential targeted action 20 in FIG. 1 .

In some embodiments, the controller 10 may execute a process 500 shownin FIG. 16 . In some embodiments, different subsets of this process 500may be executed by the illustrated components of the controller 10, sothose features are described herein concurrently. It should beemphasized, though, that embodiments of the process 500 are not limitedto implementations with the architecture of FIG. 1 , and that thearchitecture of FIG. 1 may execute processes different from thatdescribed with reference to FIG. 16 , none of which is to suggest thatany other description herein is limiting.

In some embodiments, the process 500 (and other functionality herein)may be implemented with program code or other instructions stored on atangible, non-transitory, machine-readable medium, such that when theinstructions are executed by one or more processors (a term which asused herein refers to physical processors, e.g., implemented on asemiconductor device), the described functionality is effectuated. Insome embodiments, notwithstanding use of the singular term “medium,” themedium may be distributed, with different subsets of the instructionsstored on different computing devices that effectuate those differentsubsets, an arrangement consistent with use of the singular term“medium” along with monolithic applications on a single device. In someembodiments, the described operations may be executed in a differentorder, some or all of the operations may be executed multiple times,operations may be executed concurrently with one another or multipleinstances of the described process, additional operations may beinserted, operations may be omitted, operations may be executedserially, or the processes described may otherwise be varied, again noneof which is to suggest that any other description herein is limiting.

In some embodiments, the process 500 includes obtaining, as indicated byblock 502, for a plurality of entities, datasets. The datasets may beevents or attributes involving the entities. In some embodiments, atleast a subset of the events are actions by the entities and some ofthese actions may be targeted actions.

In some embodiments, a plurality of objects may be formed, as indicatedby block 504. Objects may include events and attributes extracted fromdatasets.

In some embodiments, these datasets may be labeled with object-orientedtags, as indicated by block 506, used to classify entity logs. In someembodiments, labelling is a process of adding tags to data. Augmentingdata with labels may make it more informative and more manageable. Oneuse of labelling in object-oriented modeling may be management oflabels, not as elements in a list or table, but as objects allowing acollection of tags to be used. As objects, OO-labels may be managed andorganized through enforced set of grammar and semantic rules. Oneattribute of an OO-label may be a user facing text for sake of userexperience (UX). In some embodiments, OO-labels may be used in a singleontology used for multiple customers, each with a ML-label for theirbusiness realizing a MUPL. OO-labels may encode meta-data about adataset, control information, and governance information among otherthings.

In some embodiments, a library of classes may be formed, as indicated byblock 508. The library of classes may include the classes shown in FIG.7 . Some of the classes of the library of classes may include variousquality management systems, as indicated by block 510.

In some embodiments, validators may be CEREBRI public objects ensuring,integrity, governance, and quality objects used to ensure no datalinkage or inconsistency of datasets, ML-labels, performance (OQM) andwindows of processing. Validators may check database consistency appliedto aspects of an OOM. A function of validators may be triggeringretraining of part or complete pipelines based on quality or operationaltriggers.

In some embodiments, quality Management (QM) in an object-orientedmodeling paradigm that may be implemented as a process that integratesraw data ingestion, manipulation, transformation, composition, andstorage for building artificial intelligence models. In legacy designs,quality management may include (and in some cases may be limited to)Extract, Transform, and Load (ETL) phases of effort and to the reportingof model performance (e.g., recall, precision, F1, etc.) from an end toend perspective as a quality.

Deposition of design process and operation of models, developed usingOOM into objects, may facilitate efforts to cause quality to be embeddedin objects. Quality may be attributes of objects. Modelor, boundmodelors, and pipelines may be managed through multiple lifecyclesrather than a single one. In some embodiments, Object-Oriented QM (OQM)may have six components:

-   -   a. Data quality monitoring (DQM): DQM measures, not necessarily        exclusively (which is not to suggest that other lists are        exclusive), new or missing data source (table) or data element,        counts, mull count and unique counts, and datatype changes. DQM        may be used to figure out which data sources are reliable.    -   b. Model quality monitoring (MQM): MQM may measure, not        necessarily exclusively (which is not to suggest that other        lists are exclusive), model-based metrics, such as F1,        precision, recall, etc., or data, and triggers retraining for        drift.    -   c. Score quality monitoring (SQM): SQM may perform model        hypothesis tests, including Welch's t-test (e.g., parametric        test for equal means) and the Mann-Whitney U-test (e.g.,        non-parametric test for equal distributions). SQM may also        compute lift tables, a decile table based on the predicted        probability of positive class membership, with the cumulative        distribution function of positive cases added in. The gain chart        is a plot of the cumulative distribution function of positive        cases may be included as a function of quantile.    -   d. Label quality monitoring (LQM): Labels may be categorical and        bound by semantic rules or ontologies. LQM may be used to        understand which data sources are leverageable and impactful.        LQM may be used for data debt management and enhancing        compositions for performance.    -   e. Bias quality monitoring (BQM): Bias is a systematic        distortion of the relationship between a variable, a data set,        and results. Three types of bias may be distinguished:        information bias, selection bias, and confounding, in some        embodiments.    -   f. Private quality monitoring (PQM): Privacy may cover        personally identifiable information and access of privileged        information.

In some embodiments, a plurality of object-manipulation functions may beformed as indicated by block 512. Each of the object-manipulationobjects may be configured to leverage a specific class.

In some embodiments, an object-orientated orchestration may be formed asindicated by block 514. An orchestration may be an organization of acollection of steps that perform a specific objective when executed by acomputing system. Orchestrations may operate on objects to create newobjects and thus may be recursive.

In some embodiments, the object-orientated orchestration may then beused to select a set of actions to achieve a given targeted action. Tothis end, some embodiments may receive a request from an entity or asubscriber as indicated by block 516. Some embodiments may thendetermine the set of actions to achieve (or increase the likelihood ofachieving) the given targeted action, as indicated by block 518 in FIG.16 , while ensuring a certain level of accuracy using the qualitymanagement systems.

In some embodiments, the controller 10 may execute a process 600 shownin FIG. 17 . In some embodiments, different subsets of this process 600may be executed by the illustrated components of the controller 10, sothose features are described herein concurrently. It should beemphasized, though, that embodiments of the process 600 are not limitedto implementations with the architecture of FIG. 1 , and that thearchitecture of FIG. 1 may execute processes different from thatdescribed with reference to FIG. 17 , none of which is to suggest thatany other description herein is limiting.

In some embodiments, the process 600 includes obtaining, as indicated byblock 602, for a plurality of entities, datasets. The datasets may beevents or attributes involving the entities. In some embodiments, atleast a subset of the events are actions by the entities and some ofthese actions may be targeted actions. Some of the attributes aregovernance attributes, including the governance attributes 9003 shown inFIG. 9 .

In some embodiments, governance in CEREBRI may be a set of structures,processes and policies by which pipeline development, deployment, anduse function within an organization or set of organizations is directed,managed, and controlled to yield business value and to mitigate risk.

In some embodiments, a policy in CEREBRI may refer to set of rules,controls, and resolutions put in place to dictate model behaviorindividually as a whole. Policies are a way governance is encoded andmanaged in some embodiments. The policy items are referred to aspolicors and implemented as CEREBRI objects. As the number of rulesincrease, a policy-driven OOM may suffer from inconsistencies incurredby contradicting rules governing its behavior. In some embodiments,meta-policies (e.g., detection rules) may be used for the detection ofconflicts. In some embodiments, policy relationships may be used. Insome embodiments, attribute-based applicability spaces may be used.

In some embodiments, a plurality of objects may be formed, as indicatedby block 604. A data domain may be used to transform datasets intoobjects. Objects may include events and attributes extracted fromdatasets.

In some embodiments, these datasets may be labeled with object-orientedtags, as indicated by block 606, used to classify entity logs. Then, alibrary of classes may be formed, as indicated by block 608. The libraryof classes may include the classes shown in FIG. 7 . Also, a pluralityof object-manipulation functions may be formed as indicated by block610. Each of the object-manipulation objects may be configured toleverage a specific class.

In some embodiments, an object-orientated orchestration may be formed asindicated by block 612. An orchestration may be an organization of acollection of steps that perform a specific objective when executed by acomputing system. Orchestrations may operate on objects to create newobjects and thus may be recursive.

In some embodiments, the object-orientated orchestration may then beused to select a set of actions to achieve a given targeted actionsimilar to the process shown in FIG. 2 . To this end, some embodimentsmay receive a request from an entity or a subscriber as indicated byblock 614. Some embodiments may then determine the set of actions toachieve (or increase the likelihood of achieving) the given targetedaction, as indicated by block 616 in FIG. 17 , for instance, with apotential targeted action 20 in FIG. 1 .

The physical architecture may take a variety of forms, including asmonolithic on-premises applications executing on a single host on asingle computing device, distributed on-premise applications executingon multiple hosts on one or more local computing devices on a privatenetwork, distributed hybrid applications having on-premises componentsand other components provided in a software as a service (SaaS)architecture hosted in a remote data center with multi-tenancy accessedvia the network, and distributed SaaS implementations in which varioussubsets or all of the functionality described herein is implemented on acollection of computing devices in one or more remote data centersserving multiple tenants, each accessing the hosted functionality underdifferent tenant accounts. In some embodiments, the computing devicesmay take the form of the computing device described below with referenceto FIG. 18 .

FIG. 18 is a diagram that illustrates an exemplary computing system 1000in accordance with embodiments of the present technique. Variousportions of systems and methods described herein, may include or beexecuted on one or more computer systems similar to computing system1000. Further, processes and modules described herein may be executed byone or more processing systems similar to that of computing system 1000.

Computing system 1000 may include one or more processors (e.g.,processors 1010 a-1010 n) coupled to system memory 1020, an input/outputI/O device interface 1030, and a network interface 1040 via aninput/output (I/O) interface 1050. A processor may include a singleprocessor or a plurality of processors (e.g., distributed processors). Aprocessor may be any suitable processor capable of executing orotherwise performing instructions. A processor may include a centralprocessing unit (CPU) that carries out program instructions to performthe arithmetical, logical, and input/output operations of computingsystem 1000. A processor may execute code (e.g., processor firmware, aprotocol stack, a database management system, an operating system, or acombination thereof) that creates an execution environment for programinstructions. A processor may include a programmable processor. Aprocessor may include a Graphic Processing Unit (GPU). A processor mayinclude general or special purpose microprocessors. A processor mayreceive instructions and data from a memory (e.g., system memory 1020).Computing system 1000 may be a uni-processor system including oneprocessor (e.g., processor 1010 a), or a multi-processor systemincluding any number of suitable processors (e.g., 1010 a-1010 n).Multiple processors may be employed to provide for parallel orsequential execution of one or more portions of the techniques describedherein. Processes, such as logic flows, described herein may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating corresponding output. Processes described herein may beperformed by, and apparatus can also be implemented as, special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). Computing system 1000may include a plurality of computing devices (e.g., distributed computersystems) to implement various processing functions.

I/O device interface 1030 may provide an interface for connection of oneor more I/O devices 1060 to computer system 1000. I/O devices mayinclude devices that receive input (e.g., from a user) or outputinformation (e.g., to a user). I/O devices 1060 may include, forexample, graphical user interface presented on displays (e.g., a cathoderay tube (CRT) or liquid crystal display (LCD) monitor), pointingdevices (e.g., a computer mouse or trackball), keyboards, keypads,touchpads, scanning devices, voice recognition devices, gesturerecognition devices, printers, audio speakers, microphones, cameras, orthe like. I/O devices 1060 may be connected to computer system 1000through a wired or wireless connection. I/O devices 1060 may beconnected to computer system 1000 from a remote location. I/O devices1060 located on remote computer system, for example, may be connected tocomputer system 1000 via a network and network interface 1040.

Network interface 1040 may include a network adapter that provides forconnection of computer system 1000 to a network. Network interface 1040may facilitate data exchange between computer system 1000 and otherdevices connected to the network. Network interface 1040 may supportwired or wireless communication. The network may include an electroniccommunication network, such as the Internet, a local area network (LAN),a wide area network (WAN), a cellular communications network, or thelike.

System memory 1020 may be configured to store program instructions 1100or data 1110. Program instructions 1100 may be executable by a processor(e.g., one or more of processors 1010 a-1010 n) to implement one or moreembodiments of the present techniques. Instructions 1100 may includemodules of computer program instructions for implementing one or moretechniques described herein with regard to various processing modules.Program instructions may include a computer program (which in certainforms is known as a program, software, software application, script, orcode). A computer program may be written in a programming language,including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, or a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork.

System memory 1020 may include a tangible program carrier having programinstructions stored thereon. A tangible program carrier may include anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may include a machine-readable storagedevice, a machine-readable storage substrate, a memory device, or anycombination thereof. Non-transitory computer readable storage medium mayinclude non-volatile memory (e.g., flash memory, ROM, PROM, EPROM,EEPROM memory), volatile memory (e.g., random access memory (RAM),static random access memory (SRAM), synchronous dynamic RAM (SDRAM)),bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or thelike. System memory 1020 may include a non-transitory computer readablestorage medium that may have program instructions stored thereon thatare executable by a computer processor (e.g., one or more of processors1010 a-1010 n) to cause the subject matter and the functional operationsdescribed herein. A memory (e.g., system memory 1020) may include asingle memory device and/or a plurality of memory devices (e.g.,distributed memory devices). Instructions or other program code toprovide the functionality described herein may be stored on a tangible,non-transitory computer readable media. In some cases, the entire set ofinstructions may be stored concurrently on the media, or in some cases,different parts of the instructions may be stored on the same media atdifferent times.

I/O interface 1050 may be configured to coordinate I/O traffic betweenprocessors 1010 a-1010 n, system memory 1020, network interface 1040,I/O devices 1060, and/or other peripheral devices. I/O interface 1050may perform protocol, timing, or other data transformations to convertdata signals from one component (e.g., system memory 1020) into a formatsuitable for use by another component (e.g., processors 1010 a-1010 n).I/O interface 1050 may include support for devices attached throughvarious types of peripheral buses, such as a variant of the PeripheralComponent Interconnect (PCI) bus standard or the Universal Serial Bus(USB) standard.

Embodiments of the techniques described herein may be implemented usinga single instance of computer system 1000 or multiple computer systems1000 configured to host different portions or instances of embodiments.Multiple computer systems 1000 may provide for parallel or sequentialprocessing/execution of one or more portions of the techniques describedherein.

Those skilled in the art will appreciate that computer system 1000 ismerely illustrative and is not intended to limit the scope of thetechniques described herein. Computer system 1000 may include anycombination of devices or software that may perform or otherwise providefor the performance of the techniques described herein. For example,computer system 1000 may include or be a combination of acloud-computing system, a data center, a server rack, a server, avirtual server, a desktop computer, a laptop computer, a tabletcomputer, a server device, a client device, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a vehicle-mounted computer, or a Global Positioning System(GPS), or the like. Computer system 1000 may also be connected to otherdevices that are not illustrated, or may operate as a stand-alonesystem. In addition, the functionality provided by the illustratedcomponents may in some embodiments be combined in fewer components ordistributed in additional components. Similarly, in some embodiments,the functionality of some of the illustrated components may not beprovided or other additional functionality may be available.

Those skilled in the art will also appreciate that while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memory,elements of distributed systems, and other storage devices for purposesof memory management and data integrity. Alternatively, in otherembodiments some or all of the software components may execute in memoryon another device and communicate with the illustrated computer systemvia inter-computer communication. Some or all of the system componentsor data structures may also be stored (e.g., as instructions orstructured data) on a computer-accessible medium or a portable articleto be read by an appropriate drive, various examples of which aredescribed above. In some embodiments, instructions stored on acomputer-accessible medium separate from computer system 1000 may betransmitted to computer system 1000 via transmission media or signalssuch as electrical, electromagnetic, or digital signals, conveyed via acommunication medium such as a network or a wireless link. Variousembodiments may further include receiving, sending, or storinginstructions or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Accordingly, the presenttechniques may be practiced with other computer system configurations.

In block diagrams, illustrated components are depicted as discretefunctional blocks, but embodiments are not limited to systems in whichthe functionality described herein is organized as illustrated. Thefunctionality provided by each of the components may be provided bysoftware or hardware modules that are differently organized than ispresently depicted, for example such software or hardware may beintermingled, conjoined, replicated, broken up, distributed (e.g. withina data center or geographically), or otherwise differently organized.The functionality described herein may be provided by one or moreprocessors of one or more computers executing code stored on a tangible,non-transitory, machine readable medium. In some cases, notwithstandinguse of the singular term “medium,” the instructions may be distributedon different storage devices associated with different computingdevices, for instance, with each computing device having a differentsubset of the instructions, an implementation consistent with usage ofthe singular term “medium” herein. In some cases, third party contentdelivery networks may host some or all of the information conveyed overnetworks, in which case, to the extent information (e.g., content) issaid to be supplied or otherwise provided, the information may beprovided by sending instructions to retrieve that information from acontent delivery network.

The reader should appreciate that the present application describesseveral independently useful techniques. Rather than separating thosetechniques into multiple isolated patent applications, applicants havegrouped these techniques into a single document because their relatedsubject matter lends itself to economies in the application process. Butthe distinct advantages and aspects of such techniques should not beconflated. In some cases, embodiments address all of the deficienciesnoted herein, but it should be understood that the techniques areindependently useful, and some embodiments address only a subset of suchproblems or offer other, unmentioned benefits that will be apparent tothose of skill in the art reviewing the present disclosure. Due to costsconstraints, some techniques disclosed herein may not be presentlyclaimed and may be claimed in later filings, such as continuationapplications or by amending the present claims. Similarly, due to spaceconstraints, neither the Abstract nor the Summary of the Inventionsections of the present document should be taken as containing acomprehensive listing of all such techniques or all aspects of suchtechniques.

It should be understood that the description and the drawings are notintended to limit the present techniques to the particular formdisclosed, but to the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the present techniques as defined by the appended claims.Further modifications and alternative embodiments of various aspects ofthe techniques will be apparent to those skilled in the art in view ofthis description. Accordingly, this description and the drawings are tobe construed as illustrative only and are for the purpose of teachingthose skilled in the art the general manner of carrying out the presenttechniques. It is to be understood that the forms of the presenttechniques shown and described herein are to be taken as examples ofembodiments. Elements and materials may be substituted for thoseillustrated and described herein, parts and processes may be reversed oromitted, and certain features of the present techniques may be utilizedindependently, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the present techniques.Changes may be made in the elements described herein without departingfrom the spirit and scope of the present techniques as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” and the like mean including, but not limitedto. As used throughout this application, the singular forms “a,” “an,”and “the” include plural referents unless the content explicitlyindicates otherwise. Thus, for example, reference to “an element” or “aelement” includes a combination of two or more elements, notwithstandinguse of other terms and phrases for one or more elements, such as “one ormore.” The term “or” is, unless indicated otherwise, non-exclusive,i.e., encompassing both “and” and “or.” Terms describing conditionalrelationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,”“when X, Y,” and the like, encompass causal relationships in which theantecedent is a necessary causal condition, the antecedent is asufficient causal condition, or the antecedent is a contributory causalcondition of the consequent, e.g., “state X occurs upon condition Yobtaining” is generic to “X occurs solely upon Y” and “X occurs upon Yand Z.” Such conditional relationships are not limited to consequencesthat instantly follow the antecedent obtaining, as some consequences maybe delayed, and in conditional statements, antecedents are connected totheir consequents, e.g., the antecedent is relevant to the likelihood ofthe consequent occurring. Statements in which a plurality of attributesor functions are mapped to a plurality of objects (e.g., one or moreprocessors performing steps A, B, C, and D) encompasses both all suchattributes or functions being mapped to all such objects and subsets ofthe attributes or functions being mapped to subsets of the attributes orfunctions (e.g., both all processors each performing steps A-D, and acase in which processor 1 performs step A, processor 2 performs step Band part of step C, and processor 3 performs part of step C and step D),unless otherwise indicated. Similarly, reference to “a computer system”performing step A and “the computer system” performing step B caninclude the same computing device within the computer system performingboth steps or different computing devices within the computer systemperforming steps A and B. Further, unless otherwise indicated,statements that one value or action is “based on” another condition orvalue encompass both instances in which the condition or value is thesole factor and instances in which the condition or value is one factoramong a plurality of factors. Unless otherwise indicated, statementsthat “each” instance of some collection have some property should not beread to exclude cases where some otherwise identical or similar membersof a larger collection do not have the property, i.e., each does notnecessarily mean each and every. Limitations as to sequence of recitedsteps should not be read into the claims unless explicitly specified,e.g., with explicit language like “after performing X, performing Y,” incontrast to statements that might be improperly argued to imply sequencelimitations, like “performing X on items, performing Y on the X'editems,” used for purposes of making claims more readable rather thanspecifying sequence. Statements referring to “at least Z of A, B, andC,” and the like (e.g., “at least Z of A, B, or C”), refer to at least Zof the listed categories (A, B, and C) and do not require at least Zunits in each category. Unless specifically stated otherwise, asapparent from the discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining” or the like refer to actionsor processes of a specific apparatus, such as a special purpose computeror a similar special purpose electronic processing/computing device.Features described with reference to geometric constructs, like“parallel,” “perpendicular/orthogonal,” “square”, “cylindrical,” and thelike, should be construed as encompassing items that substantiallyembody the properties of the geometric construct, e.g., reference to“parallel” surfaces encompasses substantially parallel surfaces. Thepermitted range of deviation from Platonic ideals of these geometricconstructs is to be determined with reference to ranges in thespecification, and where such ranges are not stated, with reference toindustry norms in the field of use, and where such ranges are notdefined, with reference to industry norms in the field of manufacturingof the designated feature, and where such ranges are not defined,features substantially embodying a geometric construct should beconstrued to include those features within 15% of the definingattributes of that geometric construct. The terms “first”, “second”,“third,” “given” and so on, if used in the claims, are used todistinguish or otherwise identify, and not to show a sequential ornumerical limitation. As is the case in ordinary usage in the field,data structures and formats described with reference to uses salient toa human need not be presented in a human-intelligible format toconstitute the described data structure or format, e.g., text need notbe rendered or even encoded in Unicode or ASCII to constitute text;images, maps, and data-visualizations need not be displayed or decodedto constitute images, maps, and data-visualizations, respectively;speech, music, and other audio need not be emitted through a speaker ordecoded to constitute speech, music, or other audio, respectively.Computer implemented instructions, commands, and the like are notlimited to executable code and can be implemented in the form of datathat causes functionality to be invoked, e.g., in the form of argumentsof a function or API call. To the extent bespoke noun phrases (and othercoined terms) are used in the claims and lack a self-evidentconstruction, the definition of such phrases may be recited in the claimitself, in which case, the use of such bespoke noun phrases should notbe taken as invitation to impart additional limitations by looking tothe specification or extrinsic evidence.

In this patent, to the extent any U.S. patents, U.S. patentapplications, or other materials (e.g., articles) have been incorporatedby reference, the text of such materials is only incorporated byreference to the extent that no conflict exists between such materialand the statements and drawings set forth herein. In the event of suchconflict, the text of the present document governs, and terms in thisdocument should not be given a narrower reading in virtue of the way inwhich those terms are used in other materials incorporated by reference.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

-   1A. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors effectuate    operations comprising: obtaining, with one or more processors, for a    plurality of entities, datasets, wherein: the datasets comprise a    plurality of entity logs; the entity logs comprise events involving    the entities; at least some of the events are actions by the    entities; at least some of the actions are targeted actions; the    entity logs comprise or are otherwise associated with attributes of    the entities; and the events are distinct from the attributes;    orchestrating, with one or more processors, an object-orientated    application or service by: forming a plurality of objects, wherein    each object of the plurality of objects comprises a different set of    attributes and events; forming object-oriented labeled datasets    based on the event and the attributes of each of the datasets;    forming a library or framework of classes with a plurality of    object-orientation modelors; and forming a plurality of    object-manipulation functions, each function being configured to    leverage a respective class among the library or framework of    classes; receiving, with one or more processors, a request to    determine a set of actions to achieve, or increase likelihood of, a    given targeted action; assigning, with one or more processors, the    given targeted action to a first subset of classes from the library    or framework of classes of the object-orientated application or    service; and determining, with one or more processors, based on the    assigning, the set of actions to achieve, or increase likelihood of,    the given targeted action using a first subset of the plurality of    object-manipulation functions leveraging the first subset of classes    from the library or framework of classes of the object-orientated    application or service.-   2A. The medium of embodiment 1A, wherein the orchestrating further    comprises: adding version numbers to the datasets; adding primary    surrogate keys to the datasets and updating the version numbers; and    encoding the datasets in dimensional star schema and updating the    version numbers.-   3A. The medium of any one of embodiments 1A-2A, wherein the    orchestrating further comprises: forming a first training dataset    from the datasets; training, with one or more processors, a first    machine-learning model on the first training dataset by adjusting    parameters of the first machine-learning model to optimize a first    objective function that indicates an accuracy of the plurality of    object-orientation modelors in generating the library or framework    of classes; and storing, with one or more processors, the adjusted    parameters of the trained first machine-learning model in memory.-   4A. The medium of embodiment 3A, wherein training comprises training    with gradient descent.-   5A. The medium of any one of embodiments 1A-4A, wherein the    orchestrating further comprises: forming a second training dataset    from the datasets; training, with one or more processors, a second    machine-learning model on the second training dataset by adjusting    parameters of the second machine-learning model to optimize a second    objective function that determines the first subset of the plurality    of object-manipulation functions; and storing, with one or more    processors, the adjusted parameters of the trained second    machine-learning model in memory.-   6A. The medium of any one of embodiments 1A-5A, wherein the    plurality of entity logs comprise: consumers; communications to    consumers by an enterprise; communications to an enterprise by    consumers; purchases by consumers from an enterprise; non-purchase    interactions by consumers with an enterprise; and a customer    relationship management system of an enterprise.-   7A. The medium of embodiment 6A, wherein: the enterprise is a credit    card issuer and the given targeted action is predicting whether a    consumer will default; the enterprise is a lender and the given    targeted action is predicting whether a consumer will borrow; the    enterprise is an insurance company and the given targeted action is    predicting whether a consumer will file a claim; the enterprise is    an insurance company and the given targeted action is predicting    whether a consumer will sign-up for insurance; the enterprise is a    vehicle seller and the given targeted action is predicting whether a    consumer will purchase a vehicle; the enterprise is a seller of    goods and the given targeted action is predicting whether a consumer    will file a warranty claim, the enterprise is a wireless operator    and the given targeted action is predicting whether a consumer    upgrade their cellphone, or the enterprise is a bank and the given    targeted action is predicting GDP variation.-   8A. The medium of any one of embodiments 1A-7A, wherein the    assignment of the given targeted action to a first subset of classes    comprises: assigning the given targeted action to a first subset of    the plurality of objects using a second subset of the plurality of    object-manipulation functions; and determining the first subset of    classes from the library or framework of classes of the    object-orientated application or service that are related to the    first subset of the plurality of objects.-   9A. The medium of embodiment 8A, wherein second subset of the    plurality of object-manipulation functions are configured to add new    objects to the plurality of objects.-   10A. The medium of embodiment 9A, wherein the new objects comprise    attributes and events related to the given targeted action.-   11A. The medium of any one of embodiments 1A-10A, wherein the    datasets comprise: a data frame; a data stream; a column in a table;    a row in a column; a cell in a table; structured data; and    unstructured data.-   12A. The medium of any one of embodiments 1A-11A, wherein the    plurality of object-manipulation functions comprises: a sequence    function used to change a collection of events into a time sequences    for processing; a feature function used to gather features of a    first object-orientation modelor and then use the features in a    second object-orientation modelor; an economic function used to:    gather economic objectives and economic constraints of an entity;    and employ an allocation algorithm to maximize the objectives; and    an ensembling function used to combine a first subset of the library    or framework of classes.-   13A. The medium of embodiment 12A, wherein the plurality of    object-manipulation functions are arranged to perform in series.-   14A. The medium of embodiment 12A, wherein the plurality of    object-manipulation functions are arranged to change orders    dynamically based on the given targeted action.-   15A. The medium of any one of embodiments 1A-14A, wherein the    plurality of object-orientation modelors comprises: a scaled    propensity modelor used to calculate probability of a customer    making an economic commitment; a timing modelor used to calibrate    moments in time when a customer is likely to engage with the given    targeted action; an affinity modelor used to capture ranked likes    and dislikes of an entity's customers for a first subset of targeted    actions; a best action modelor used to create a framework for    concurrent Key Performance Index of the given targeted action at    different points in a customer's journey; and a cluster modelor used    to group an entity's customers based on the customers' behavior into    a finite list.-   16A. The medium of embodiment 15A, wherein the plurality of    object-orientation modelors are arranged to perform in series or in    parallel.-   17A. The medium of embodiment 15A, wherein the plurality of    object-orientation modelors are arranged to change orders    dynamically based on the given targeted action.-   18A. The medium of any one of embodiments 1A-17A, wherein: the given    targeted action comprises a plurality of sub-targets; and at least    some targets of the plurality of sub-targets are expected to happen    at different times in future.-   19A. The medium of any one of embodiments 1A-18A, wherein: the given    targeted action comprises a plurality of sub-targets; and the    plurality of object-orientation modelors comprises: a scaled    propensity modelor used to calculate probability of a customer    making an economic commitment; a timing modelor used to calibrate    moments in time when a customer is likely to engage with each subset    of the plurality of sub-targets; an affinity modelor used to capture    ranked likes and dislikes of an entity's customers for a first    subset of targeted actions; a best action modelor used to create a    framework for concurrent Key Performance Index for each subset of    the plurality of sub-targets at different points in a customer's    journey; a cluster modelor used to group an entity's customers based    on the customers' behavior into a finite list; and wherein: a first    subset of the plurality of object-orientation modelors are used for    a first subset of the plurality of sub-targets; a second subset of    the plurality of object-orientation modelors are used for a second    subset of the plurality of sub-targets; and wherein the order in    which the first subset of the plurality of object-orientation    modelors perform is different from the order in which the second    subset of the plurality of object-orientation modelors perform.-   20A. The medium of any one of embodiments 1A-10A, wherein the    object-oriented labeled datasets formed according to an ontology of    events.-   21A. The medium of embodiment 20A, wherein the ontology of events    comprises Concurrent Ontology Labelling Datastore (COLD)    methodology.-   22A. The medium of any one of embodiments 1A-21A, wherein the    object-oriented labeled datasets formed according to a hierarchal    taxonomy of events.-   1B. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors effectuate    operations comprising: identifying, with one or more processors,    feature processing transformations of one or more datasets;    identifying, with one or more processors, pipelines of the    transformations, the pipelines being pipelines in an object-oriented    modeling application; forming, with one or more processors, a first    plurality of classes using object-oriented modeling of the feature    processing transformations of the one or more datasets, the first    plurality of classes being classes of objects in the object-oriented    modeling application; forming, with one or more processors, a second    plurality of classes using object-oriented modeling of the    pipelines, the second plurality of classes being classes of objects    in the object-oriented modeling application; forming, with one or    more processors, a third plurality of classes using object-oriented    modeling of the one or more datasets, the third plurality of classes    being classes of objects in the object-oriented modeling    application; interrogating, with one or more processors, a class    library containing the first plurality of classes to determine first    class definition information; interrogating, with one or more    processors, a class library containing the second plurality of    classes to determine second class definition information; selecting,    with one or more processors, a given dataset from the one or more    datasets or other datasets; interrogating, with one or more    processors, a class library containing the third plurality of    classes to determine third class definition information; accessing,    with one or more processors, the first, second, and third class    definitions information to produce an interdependency graph of one    or more data processing operator instances of a given pipeline among    the pipelines of the transformations; generating, with one or more    processors, an execution schedule of the given pipeline based on the    interdependency graph; causing, with one or more processors,    execution of the given pipeline according to the execution schedule;    accessing the first definition information to process the given    dataset; and storing a result of processing the given dataset in    memory.-   2B. The medium of embodiment 1B, wherein the operations further    comprise: accessing attributes of the given dataset at each of a    plurality of modelors of the given pipeline.-   3B. The medium of any one of embodiments 1B-2B, wherein causing    execution of the given pipeline according to the execution schedule    comprises: executing the given pipeline on a distributed cluster    computing framework.-   4B. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors effectuate    operations comprising: writing, with one or more processors, a first    plurality of classes using object-oriented modelling; writing, with    one or more processors, a second plurality of classes using    object-oriented modelling of modeling topics; scanning, with one or    more processors, a class library containing the first plurality of    classes to determine class definition information; scanning, with    one or more processors, a class library containing the second    plurality of classes to determine class definition information;    receiving, with one or more processors, at an orchestrating system,    from a subscribing modeling object a request for a subscription to a    given modeling topic in a given modeling topic class among the    second plurality of classes, the subscription request including a    modeling topic filter to select the given modeling topic from a    plurality of modeling topics described by the given modeling topic    class; registering, with one or more processors, by the    orchestrating system, a modeling topic accessor associated with the    subscribing modeling object; registering, with one or more    processors, by the orchestrating system, a modeling topic mutator    associated with the subscribing modeling object; processing, with    one or more processors, by the orchestrating system, through the    modeling topic filter a modeling topic that is accessed through an    accessor and is described by the modeling topic class, the modeling    topic being received from a modeling publisher object; notifying,    with one or more processors, by the orchestrating system, the    subscribing object of the received modeling topic through a    registered modeling topic listener, in response to determining that    the received modeling topic matches the modeling topic filter; and    mutating, with one or more processors, the received modeling topic    at the subscriber modeling object in response to determining that    the received modeling topic matches the modeling topic filter    included in the request for a subscription.-   5B. The medium of embodiment 4B, wherein mutating comprises: adding    an attribute to an object, deleting an attribute of an object,    updating an attribute of an object, reading an attribute an object,    adding reference to another object as an attribute, using a setter,    or using a getter.-   6B. The medium of any one of embodiments 4B-5B, wherein mutating    comprises: adding an attribute to an object, deleting an attribute    of an object, updating an attribute of an object, reading an    attribute an object, adding reference to another object as an    attribute, using a setter, and using a getter.-   7B. The medium of any one of embodiments 4B-6B, wherein: items    captured in modeling topics include: consumers, communications to    consumers by an enterprise, communications to an enterprise by    consumers, events that include purchases by consumers from the    enterprise, and events that include non-purchase interactions by    consumers with the enterprise; and at least some items are obtained    from entity logs that are obtained from a customer relationship    management system of the enterprise.-   8B. The medium of embodiment 7B, wherein: a result of the operations    is used by a trained predictive machine learning model developed    using an object-oriented modeling (OOM) framework.-   9B. The medium of embodiment 8B, wherein: the enterprise is a credit    card issuer and the trained predictive machine learning model    developed using the OOM framework is configured to predict whether a    consumer will default; the enterprise is a lender and the trained    predictive machine learning model developed using the OOM framework    is configured to predict whether a consumer will borrow; the    enterprise is an insurance company and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer will file a claim; the    enterprise is an insurance company and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer will sign-up for insurance;    the enterprise is a vehicle seller and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer will purchase a vehicle;    the enterprise is a seller of goods and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer will file a warranty claim,    or the enterprise is a wireless operator and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer upgrade their cellphone, or    the enterprise is bank and the trained predictive machine learning    model developed using the OOM framework is configured to predict the    change in GDP.-   10B. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors effectuate    operations comprising: obtaining, with one or more processors, for a    plurality of entities, datasets, wherein: the datasets comprise a    plurality of entity logs; a first subset of the plurality of entity    logs are events involving the entities; a first subset of the events    are actions by the entities; at least some of the actions are    targeted actions; a second subset of the plurality of entity logs    are attributes related to the entities; and the events are distinct    from the attributes; forming, with one or more processors, an    object-orientated orchestration, the object-orientated orchestration    comprising: forming a plurality of objects, wherein each object of    the plurality of objects comprises a different set of attributes and    events; forming object-oriented labeled datasets based on the event    and the attributes of each of the datasets; forming a library of    classes, generated by a plurality of object-orientation modelors;    and forming a plurality of object-manipulation functions, each    function configured to leverage a specific class; forming a first    training dataset from the datasets; training, with one or more    processors, a first machine-learning model on the first training    dataset by adjusting parameters of the first machine-learning model    to optimize a first objective function that indicates    interdependency of the plurality of object-manipulation functions in    leveraging a specific class; forming an interdependency graph using,    at least in part, the first objective function; and storing, with    one or more processors, the adjusted parameters of the trained first    machine-learning model in memory; receiving a request to determine a    set of actions required to achieve a specific targeted action;    determining, with one or more processors, the set of actions    required to achieve the specific targeted action using a compiler    function, the compiler function comprising: assigning the specific    targeted action to a first subset of the plurality of objects using    a first subset of the plurality of object-manipulation functions,    wherein: the first subset of the plurality of object-manipulation    functions is formed using the interdependency graph; and determining    a first subset of classes from the library of classes of the    object-oriented orchestration that are related to the first subset    of the plurality of objects; determining the set of actions required    to achieve the specific targeted action using a second subset of the    plurality of object-manipulation functions, wherein: the second    subset of the plurality of object-manipulation functions is formed    using the interdependency graph; and each object-manipulation    function of the second subset of the plurality of    object-manipulation functions is configured to leverage at least one    class of the first subset of classes from the library of classes of    the object-oriented orchestration.-   11B. The medium of embodiment 10B, wherein the interdependency graph    comprises: a plurality of execution schedules, wherein each    execution schedule from the plurality of execution schedules    comprises a subset of the object-manipulation functions.-   12B. The medium of embodiment 11B, wherein each execution schedule    from the plurality of execution schedules is configured to leverage    at least one class from the library of classes.-   13B. The medium of any one of embodiments 10B-12B, wherein the    interdependency graph comprises: a plurality of execution triggers,    wherein each execution trigger from the plurality of execution    triggers comprises a subset of the object-manipulation functions.-   14B. The medium of any one of embodiments 10B-13B, wherein the    interdependency graph comprises: a plurality of execution schedules,    wherein each execution schedule from the plurality of execution    schedules comprises a subset of the object-manipulation functions, a    plurality of execution triggers, wherein each execution trigger from    the plurality of execution triggers comprises a subset of the    object-manipulation functions, and an orchestrator assigning    execution triggers or execution schedules within the interdependency    graph.-   15B. The medium of any one of embodiments 10B-14B, wherein the    formation object-orientated orchestration further comprises: forming    a second training dataset from the datasets; training, with one or    more processors, a second machine-learning model on the second    training dataset by adjusting parameters of the second    machine-learning model to optimize a second objective function that    determines the first subset of the plurality of object-manipulation    functions; and storing, with one or more processors, the adjusted    parameters of the trained second machine-learning model in memory.-   16B. The medium of any one of embodiments 10B-15B, comprising    indicating interdependency of the plurality of object-manipulation    functions in leveraging a specific class with the first trained    machine learning at least in part by: obtaining a given entity log    of the given entity; determining a plurality of features from the    given entity log, the plurality of features having fewer dimensions    than the given entity log; and inputting the determined plurality of    features into the first trained machine learning model to cause the    model to output a value indicative of the interdependency of the    plurality of object-manipulation functions in leveraging a specific    class related to the given entity.-   17B. The medium of any one of embodiments 10B-16B, wherein: the    first machine learning model is based on a plurality of decision    trees combined with an ensemble procedure; and the ensemble    procedure is boosting, random forest or other form of bootstrap    aggregation, or rotation forest; and at least some of the decision    trees are trained with classification and regression tree by    recursively splitting a feature space of inputs to the first machine    learning model along different dimensions of the feature space at    values of respective dimensions that locally optimize the respective    split to minimize entropy of Gini impurity of targeted actions and    non-targeted actions on each side of respective splits.-   18B. The medium of any one of embodiments 10B-17B, comprising,    before training, transforming each entity log into a collection of    features to which the first machine learning model is capable of    responding and training the model on features of the collection of    features.-   19B. The medium of embodiment 18, wherein at least some of the    features are determined by the attributes, the attributes    comprising: entity restrictions for at least some of the plurality    of entities; entity business protocols for at least some of the    plurality of entities; entity policies for at least some of the    plurality of entities; entity authorized users for at least some of    the plurality of entities; and entity security protocols for at    least some of the plurality of entities.-   20B. The medium of any one of embodiments 10B-19B, wherein training    comprises means for training.-   21B. The medium of any one of embodiments 10B-20B, wherein the    plurality of entity logs comprise information about: consumers;    communications to consumers by an enterprise; communications to an    enterprise by consumers; purchases by consumers from an enterprise;    non-purchase interactions by consumers with an enterprise; and a    customer relationship management system of an enterprise.-   22B. The medium of any one of embodiments 10B-21B, wherein: the    enterprise is a credit card issuer and the specific targeted action    is predicting whether a consumer will default; the enterprise is a    lender and the specific targeted action is predicting whether a    consumer will borrow; the enterprise is an insurance company and the    specific targeted action is predicting whether a consumer will file    a claim; the enterprise is an insurance company and the specific    targeted action is predicting whether a consumer will sign-up for    insurance; the enterprise is a vehicle seller and the specific    targeted action is predicting whether a consumer will purchase a    vehicle; the enterprise is a seller of goods and the specific    targeted action is predicting whether a consumer will file a    warranty claim, the enterprise is a wireless operator and the    specific targeted action is predicting whether a consumer upgrade    their cellphone, or the enterprise is a bank and the specific    targeted action is predicting GDP variation.-   23B. The medium of any one of embodiments 10B-22B, wherein the first    trained model is configured to filter some of the entity logs,    wherein the filtration comprise: a dependency level among the entity    logs calculated by Bayesian Networks; a logistic regression    calculated by Lasso and ElasticNet penalty functions; or a product    moment correlation coefficient calculated by Pearson correlation.-   24B. The medium of any one of embodiments 10B-23B, wherein the    object-oriented labeled datasets formed according to an ontology of    events.-   25B. The medium of any one of embodiments 10B-24B, wherein the    object-oriented labeled datasets formed according to a hierarchal    taxonomy of events.-   26B. The medium of any one of embodiments 10B-24B, the operations    further comprising: steps for determining the set of actions    required to achieve the specific targeted action.-   27B. The medium of any one of embodiments 10B-26B, wherein the    operations further comprise: formation of the interdependency graph    using, at least in part, feature engineering modelors, the feature    engineering modelors comprise: recency feature engineering modelors;    frequency feature engineering modelors; lag feature engineering    modelors; difference feature engineering modelors; and harmonic    analysis feature engineering modelors; wherein the feature    engineering modelors are a subset of plurality of object-orientation    modelors.-   28B. The medium of any one of embodiments 10B-27B, wherein the    plurality of object-manipulation functions comprise: a feature    engineering function used to gather features of a first    object-orientation modelor and then use the features in a second    object-orientation modelor, wherein the feature engineering function    comprises: a recency feature engineering sub-routine; a frequency    feature engineering sub-routine; a lag feature engineering    sub-routine; a difference feature engineering sub-routine; and a    harmonic analysis feature engineering sub-routine.-   29B. The medium of any one of embodiments 10B-28B, wherein the    plurality of object-orientation modelors comprise: a scaled    propensity modelor used to calculate probability of a customer    making an economic commitment; a timing modelor used to calibrate    moments in time when a customer is likely to engage with the    specific targeted action; an affinity modelor used to capture ranked    likes and dislikes of an entity's customers for a first subset of    targeted actions; a best action modelor used to create a framework    for concurrent Key Performance Index of the specific targeted action    at different points in a customer's journey; and a cluster modelor    used to group an entity's customers based on the customers' behavior    into a finite list.-   30B. The medium of any one of embodiments 10B-29B, the operations    further comprising forming an object-orientated orchestration by:    adding version indicators to the datasets; adding primary surrogate    keys to the datasets and updating the version indicators; and    encoding the datasets in dimensional star schema and updating the    version indicators.-   31B. The medium of any one of embodiments 10B-30B, the operations    further comprising forming an object-orientated orchestration by:    adding version indicators to the modelors.-   1C. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors effectuate    operations comprising: forming, with one or more processors, a first    plurality of classes using object-oriented modelling of modelling    objects, the modelling objects being used to implement a machine    learning design based on optimization criteria using an    object-oriented modeling (OOM) framework, the first plurality of    classes being members of a first class library; forming, with one or    more processors, a second plurality of classes using object-oriented    modelling of an orchestration of the machine learning design, the    second plurality of classes being members of a second class library;    forming, with one or more processors, a third plurality of classes    using object-oriented modelling of the optimization criteria, the    optimization criteria being used to optimize an orchestration of the    modelling objects, the third plurality of classes being members of a    third class library; forming, with one or more processors, a fourth    plurality of classes using object-oriented modelling of the    optimization of the orchestration of the modelling objects, the    fourth plurality of classes being members of a fourth class library;    forming, with one or more processors, a fifth plurality using    object-oriented modelling of an optimization value, the fifth    plurality of classes being members of a fifth class library;    accessing, with one or more processors, an optimization criterion    object from the third class library; accessing, with one or more    processors, the third class library to determine first class    definition information, the first class definition information being    class definition information of the optimization criterion object;    accessing, with one or more processors, the first class library to    determine second class definition information; accessing, with one    or more processors, the fourth class to determine third class    definition information; using, with one or more processors, at least    some of the first, second, or third class definition information to    form a sequence of object manipulation function to effectuate access    by an orchestration system to methods and attributes of at least    some of the first, second, third, fourth, or fifth plurality of    classes to manipulate objects of the first plurality of classes;    using, with one or more processors, at least some of the first,    second, or third class definition information to effectuate access    to the object manipulation functions by the orchestration system;    processing, with one or more processors, orchestration of modelling,    the orchestration including statements seeking access to one or more    modelling object classes among the first plurality of classes within    the first class library; processing, with one or more processors, an    optimization method of orchestration, the optimization method    including statements seeking access to one or more orchestration    object classes among the second plurality of classes within the    second class library; processing, with one or more processors,    optimization criteria of the optimization method, the optimization    criteria including statements seeking access to one or more    orchestration object classes among the second plurality of classes    within the second class library; and invoking, with one or more    processors, previously formed object manipulation functions using at    least some of the sought access to activate the object manipulation    functions during implementation by the optimization system of the    optimization method, thereby causing use of the optimization to    access modelling object classes among the first plurality of    classes.-   2C. The medium of embodiment 1C, wherein the instructions are    executed in the optimization system to execute optimization the    orchestration of modelling objects for implementation of the machine    learning design based on optimization criteria using an    object-oriented modeling (OOM) framework.-   3C. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors in a    computing system to effectuate operations to execute the processing    of modeling methods organized in racks of a machine learning    pipeline to facilitate optimization of performance using modelling    methods for implementation of machine learning design in an    object-oriented modeling (OOM) framework, the operations comprising:    writing, with the computer system, a first plurality of classes of a    first class library using object-oriented modelling of optimization    methods; writing, with the computer system, a second plurality of    classes of a second class library using object-oriented modelling of    said modelling methods; writing, with the computer system,    parameters and hyper-parameters of the modeling methods as    attributes as the modeling methods; writing, with the computer    system, a plurality of classes of a third class library using    object-oriented modelling of the modelling racks; scanning, with the    computer system, modelling racks classes among the third plurality    of classes in the third class library to determine first class    definition information; selecting, with the computer system, a    collection of one or more racks among the racks of the machine    learning pipeline; for each rack in the collection, with the    computer system, selecting one or more modeling method objects;    scanning, with the computer system, modelling method classes among    the second plurality of classes in the second class library to    determine second class definition information; assigning, with the    computer system, one or more racks and locations within the one or    more racks to corresponding selected modeling method objects;    invoking, with the computer system, at least some of the second    class definition information of modeling method classes and at least    some of the first class definition information of the modeling racks    to produce object manipulation functions that allow the computer    system to access the methods and attributes of at least some of the    modeling method objects, at least some of the manipulation functions    being configured to effectuate writing locations within racks and    attributes of racks; selecting, with the computer system, an    optimization method among the optimization methods; and creating,    with the computer system, an optimization object.-   4C. The medium of embodiment 3C, the operations further comprising:    invoking the class definition information of at least one modeling    method class and at least some class definition information of at    least one modeling rack and at least some class definition    information of at least one optimization method to produce    additional object manipulation functions that allow the computing    system to access methods and attributes of a corresponding    optimization object to manipulate the corresponding optimization    object; and invoking the class definition of the least one    optimization method to optimize the corresponding optimization    object.-   5C. The medium of any one of embodiments 3C-4C, wherein: the    operations further comprise processing data construct objects based    on entity logs; entities captured in the data construct objects    comprise: consumers, communications to consumers by an enterprise,    communications to an enterprise by consumers, and events that    include purchases by consumers from the enterprise and non-purchase    interactions by consumers with the enterprise; and the entity logs    are obtained from a customer relationship management system of the    enterprise.-   6C. The medium of embodiment 5C, wherein: the enterprise is a credit    card issuer and a trained predictive machine learning model    developed using the object-oriented modeling (OOM) framework is    configured to predict whether a consumer will default; the    enterprise is a lender and the trained predictive machine learning    model developed using the OOM framework is configured to predict    whether a consumer will borrow; the enterprise is an insurance    company and the trained predictive machine learning model developed    using the OOM framework is configured to predict whether a consumer    will file a claim; the enterprise is an insurance company and the    trained predictive machine learning model developed using the OOM    framework is configured to predict whether a consumer will sign-up    for insurance; the enterprise is a vehicle seller and the trained    predictive machine learning model developed using the OOM framework    is configured to predict whether a consumer will purchase a vehicle;    the enterprise is a seller of goods and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer will file a warranty claim;    the enterprise is a wireless operator and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer upgrade their cellphone; or    the enterprise is bank and the trained predictive machine learning    model developed using the OOM framework is configured to predict the    change in GDP.-   7C. The medium of any one of embodiments 3C-6C, wherein: the    operations further comprise processing data construct objects based    on entity logs; entities captured in the data construct objects    comprise: consumers, product information, service information,    events that include purchases by consumers from the enterprise and    non-purchase interactions by consumers with the enterprise, and    events that include subscriptions by consumers from the enterprise    and non-purchase interactions by consumers with the enterprise; and    the entity logs are obtained from a customer relationship management    system of the enterprise.-   8C. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors effectuate    operations comprising: obtaining, with one or more processors, for a    plurality of entities, datasets, wherein: the datasets comprise a    plurality of entity logs; a first subset of the plurality of entity    logs are events involving the entities; a first subset of the events    are actions by the entities; at least some of the actions are    targeted actions; a second subset of the plurality of entity logs    are attributes related to the entities; and the events are distinct    from the attributes; forming, with one or more processors, an    object-orientated orchestration by: forming a plurality of objects,    wherein each object of the plurality of objects comprises a    different set of attributes and events; forming object-oriented    labeled datasets based on the event and the attributes of each of    the datasets; forming a library of classes, generated by a plurality    of object-orientation modelors; and forming a plurality of    object-manipulation functions, each function configured to leverage    a specific class; receiving, with one or more processors, a request    to determine a set of actions required to achieve a specific    targeted action; and determining, with one or more processors, the    set of actions required to achieve the specific targeted action    using a compiler function, the compiler function comprising    instructions to effectuate: assigning the specific targeted action    to a first subset of the plurality of objects using a first subset    of the plurality of object-manipulation functions; and determining a    first subset of classes from the library of classes of the    object-oriented orchestration that are related to the first subset    of the plurality of objects; determining the set of actions required    to achieve the specific targeted action using a second subset of the    plurality of object-manipulation functions, wherein: each    object-manipulation function of the second subset of the plurality    of object-manipulation functions is configured to leverage at least    one class of the first subset of classes from the library of classes    of the object-oriented orchestration.-   9C. The medium of embodiment 8C, wherein the compiler function    further comprising instructions to effectuate: forming a first    training dataset from the datasets; training a first    machine-learning model on the first training dataset by adjusting    parameters of the first machine-learning model to optimize a first    objective function that indicates which object-manipulation    functions from the plurality of object-manipulation functions should    be included in the second subset of the plurality of    object-manipulation functions; and storing the adjusted parameters    of the trained first machine-learning model in memory.-   10C. The medium of embodiment 9C, wherein: the first machine    learning model comprises a Hidden Markov model.-   11C. The medium of embodiment 9C, wherein: the first machine    learning model comprises a long short-term memory model.-   12C. The medium of embodiment 9C, wherein: the first machine    learning model comprises a dynamic Bayesian network.-   13C. The medium of embodiment 9C, wherein: the first machine    learning model comprises a neural network classifier.-   14C. The medium of embodiment 9C, wherein: the first machine    learning model is part of a value function or an environment model    of a reinforcement learning model.-   15C. The medium of embodiment 9C, wherein training comprises steps    for training.-   16C. The medium of embodiment 9C, wherein the first dataset    comprises: the first subset of the plurality of objects.-   17C. The medium of embodiment 9C, comprising: inputting more than    1,000 entity logs corresponding to more than 1,000 entities input    into the first trained machine learning model.-   18C. The medium of any one of embodiments 8C-17C, wherein the    compiler function further comprising instructions to effectuate:    forming a first training dataset from the datasets; training a first    machine-learning model on the first training dataset by adjusting    parameters of the first machine-learning model to optimize a first    objective function that indicates which object-manipulation    functions from the plurality of object-manipulation functions should    be included in the second subset of the plurality of    object-manipulation functions; and storing the adjusted parameters    of the trained first machine-learning modelling pipeline in memory.-   19C. The medium of any one of embodiments 8C-18C, wherein the    compiler function further comprising instructions to effectuate:    forming a second training dataset from the datasets; training a    second machine-learning model on the second training dataset by    adjusting parameters of the second machine-learning model to    optimize a second objective function that indicates the order of    operation of each object-manipulation function in the second subset    of the plurality of object-manipulation functions; and storing the    adjusted parameters of the trained second machine-learning model in    memory.-   20C. The medium of any one of embodiments 8C-19C, wherein the    plurality of entity logs comprise information about: consumers;    communications to consumers by an enterprise; communications to an    enterprise by consumers; purchases by consumers from an enterprise;    non-purchase interactions by consumers with an enterprise; and a    customer relationship management system of an enterprise.-   21C. The medium of embodiment 20C, wherein: the enterprise is a    credit card issuer and the specific targeted action is predicting    whether a consumer will default; the enterprise is a lender and the    specific targeted action is predicting whether a consumer will    borrow; the enterprise is an insurance company and the specific    targeted action is predicting whether a consumer will file a claim;    the enterprise is an insurance company and the specific targeted    action is predicting whether a consumer will sign-up for insurance;    the enterprise is a vehicle seller and the specific targeted action    is predicting whether a consumer will purchase a vehicle; the    enterprise is a seller of goods and the specific targeted action is    predicting whether a consumer will file a warranty claim, the    enterprise is a wireless operator and the specific targeted action    is predicting whether a consumer upgrade their cellphone, or the    enterprise is a bank and the specific targeted action is predicting    GDP variation.-   22C. The medium of embodiment 20C, wherein the second subset of the    plurality of object-manipulation functions further comprises: an    economic optimization function with a plurality of function    parameters, wherein the plurality of function parameters are    adjusted based on business objectives of the enterprise.-   23C. The medium of embodiment 22C, wherein the business objectives    of the enterprise comprise: increase in revenue; increase in profit    margin; or reduction in cost; wherein each of the business    objectives has a set business constraints.-   24C. The medium of embodiment 23C, wherein the at least some of the    sets business constraints comprise: a plurality of specific age    groups; a specific product or service; a window of time; and a    plurality of geographical locations.-   25C. The medium of embodiment 23C, wherein the at least some of the    sets business constraints comprise: an amount of time to optimize    operation; and an amount of hardware used to optimize operation.-   26C. The medium of any one of embodiments 8C-25C, wherein: the    specific targeted comprises a plurality of sub-targets; and the    plurality of object-orientation modelors comprises: a scaled    propensity modelor used to calculate probability of a customer    making an economic commitment; a timing modelor used to calibrate    moments in time when a customer is likely to engage with each subset    of the plurality of sub-targets; an affinity modelor used to capture    ranked likes and dislikes of an entity's customers for a first    subset of targeted actions; a best action modelor used to create a    framework for concurrent Key Performance Index for each subset of    the plurality of sub-targets at different points in a customer's    journey; a cluster modelor used to group an entity's customers based    on the customers' behavior into a finite list; and wherein: a first    subset of the plurality of object-orientation modelors are used for    a first subset of the plurality of sub-targets; a second subset of    the plurality of object-orientation modelors are used for a second    subset of the plurality of sub-targets; and wherein the order in    which the first subset of the plurality of object-orientation    modelors perform is different from the order in which the second    subset of the plurality of object-orientation modelors perform.-   1D. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors effectuate    operations comprising: writing, with a computing system, a first    plurality of classes using object-oriented modelling of modelling    methods; writing, with the computing system, a second plurality of    classes using object-oriented modelling of governance; scanning,    with the computing system, a set of libraries collectively    containing both modelling object classes among the first plurality    of classes and governance classes among the second plurality of    classes to determine class definition information; using, with the    computing system, at least some of the class definition information    to produce object manipulation functions, wherein the object    manipulation functions allow a governance system to access methods    and attributes of classes among first plurality of classes or the    second plurality of classes to manipulate objects of at least some    of the modelling object classes; and using at least some of the    class definition information to effectuate access to the object    manipulation functions.-   2D. The medium of embodiment 1D, wherein: the operations execute    quality management of modelling methods for implementation of    machine learning design in an object-oriented modeling (OOM)    framework.-   3D. The medium of any one of embodiments 1D-2D, wherein: the modeled    governance comprises a set of structures, processes, or policies by    which pipeline development, deployment, or use functionality within    an organization or set of organizations is directed, managed, or    controlled.-   4D. The medium of any one of embodiments 1D-3D, wherein: the modeled    governance comprises a set of structures, processes, and policies by    which pipeline development, deployment, and use functionality within    an organization or set of organizations is directed, managed, and    controlled.-   5D. The medium of any one of embodiments 1D-4D, wherein: the modeled    governance comprises a policy; and the policy comprises a set of    rules, controls, or resolutions put in place to dictate model    behavior.-   6D. The medium of any one of embodiments 1D-5D, wherein: the modeled    governance comprises a policy; and the policy comprises a set of    rules, controls, or resolutions put in place to dictate model    versioning.-   7D. The medium of any one of embodiments 1D-6D, wherein: the modeled    governance comprises a set of policors; and meta-policies having    detection rules are used to detect conflicts among policors.-   8D. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors in a    computing system effectuate operations to manage compliance    governance using modelling methods in a pipeline for implementation    of machine learning design in an object-oriented modeling (OOM)    framework, the operations comprising: forming, with one or more    processors, a first plurality of classes using object-oriented    modelling of the modelling methods; forming, with one or more    processors, a second plurality of classes using object-oriented    modelling of governance compliance methods; scanning, with one or    more processors, a class library containing modelling method classes    to determine a first part of class definition information; scanning,    with one or more processors, a class library containing governance    compliance classes to determine a second part of class definition    information; and using, with one or more processors, the first part    of class definition information of the modeling method class and the    second part of class definition information of the management method    class to produce object manipulation functions that allow the    computing system to access methods and attributes of a governance    compliance object to manipulate a method class object.-   9D. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors in a    computing system effectuate operations to measure quality of    processing of data constructs in a pipeline using modelling methods    for implementation of machine learning design in an object-oriented    modeling (OOM) framework, the operations comprising: writing, with    the computing system, a first plurality of classes using    object-oriented modelling of the modelling methods; writing, with    the computing system, a second plurality of classes using    object-oriented modelling of quality measurement methods; scanning,    with the computing system, a class library containing modelling    method classes to determine a first part of class definition    information; scanning, with the computing system, another class    library containing quality management classes to determine a second    part of class definition information; and invoking, with the    computing system, the class definition information to produce object    manipulation functions that allow the computing system to access    methods and attributes of data classes to manipulate a modeling    method class.-   10D. The medium of embodiment 9D, wherein: the quality measurement    methods comprise data quality monitoring (DQM), model quality    monitoring (MQM), score quality monitoring (SQM), bias quality    management (BQM), privacy quality management (PQM), or label quality    monitoring (LQM).-   11D. The medium of any one of embodiments 9D-10D, wherein: the    operations comprise object manipulation by allowing reading of    attributes, usage of a given modeling method, audit of usage of a    given modeling object, reporting attempts to use the given modeling    object, or verifying proper licensing.-   12D. The medium of any one of embodiments 9D-11D, wherein: the    operations comprise object manipulation by allowing reading of    attributes, usage of a given modeling method, audit of usage of a    given modeling object, reporting attempts to use the given modeling    object, and verifying proper licensing; and the quality measurement    methods comprise data quality monitoring (DQM), model quality    monitoring (MQM), score quality monitoring (SQM), bias quality    management (BQM), privacy quality management (PQM), and label    quality monitoring (LQM).-   13D. The medium any one of embodiments 9D-12D, wherein: the    operations further comprise processing data construct objects based    on entity logs; entities captured in the data construct objects    comprise: consumers, communications to consumers by an enterprise,    communications to an enterprise by consumers, and events that    include purchases by consumers from the enterprise and non-purchase    interactions by consumers with the enterprise; and the entity logs    are obtained from a customer relationship management system of the    enterprise.-   14D. The medium any one of embodiments 9D-13D, wherein: the    enterprise is a credit card issuer and a trained predictive machine    learning model developed using the object-oriented modeling (OOM)    framework is configured to predict whether a consumer will default;    the enterprise is a lender and the trained predictive machine    learning model developed using the OOM framework is configured to    predict whether a consumer will borrow; the enterprise is an    insurance company and the trained predictive machine learning model    developed using the OOM framework is configured to predict whether a    consumer will file a claim; the enterprise is an insurance company    and the trained predictive machine learning model developed using    the OOM framework is configured to predict whether a consumer will    sign-up for insurance; the enterprise is a vehicle seller and the    trained predictive machine learning model developed using the OOM    framework is configured to predict whether a consumer will purchase    a vehicle; the enterprise is a seller of goods and the trained    predictive machine learning model developed using the OOM framework    is configured to predict whether a consumer will file a warranty    claim; the enterprise is a wireless operator and the trained    predictive machine learning model developed using the OOM framework    is configured to predict whether a consumer upgrade their cellphone;    or the enterprise is bank and the trained predictive machine    learning model developed using the OOM framework is configured to    predict the change in GDP.-   15D. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors effectuate    operations comprising: obtaining, with one or more processors, for a    plurality of entities, datasets, wherein: the datasets comprise a    plurality of entity logs; a first subset of the plurality of entity    logs are events involving the entities; a first subset of the events    are actions by the entities; at least some of the actions are    targeted actions; a second subset of the plurality of entity logs    are attributes related to the entities; a first subset of the    attributes are governance attributes; and the events are distinct    from the attributes; forming, with one or more processors, an    object-orientated orchestration, the object-orientated orchestration    comprising: forming a plurality of objects, wherein each object of    the plurality of objects comprises a different set of attributes and    events; forming object-oriented labeled datasets based on the event    and the attributes of each of the datasets; forming a library of    classes, generated by a plurality of object-orientation modelors;    and forming a plurality of object-manipulation functions, each    function configured to leverage a specific class; receiving a    request to determine a set of actions required to achieve a specific    targeted action; assigning the specific targeted action to a first    subset of classes from the library of classes of the object-oriented    orchestration; and determining, with one or more processors, the set    of actions required to achieve the specific targeted action using a    first subset of the plurality of object-manipulation functions    related to the first subset of classes from the library of classes    of the object-oriented orchestration.-   16D. The medium of embodiment 15D, wherein the governance attributes    comprises: entity restrictions for at least some of the plurality of    entities; entity business protocols for at least some of the    plurality of entities; entity policies for at least some of the    plurality of entities; entity authorized users for at least some of    the plurality of entities; and entity security protocols for at    least some of the plurality of entities.-   17D. The medium of any one of embodiments 15D-16D, wherein: a first    subset of the plurality of object-orientation modelors are    governance modelors; the governance modelors are configured to form    governance classes; and a second subset of the plurality of    object-manipulation functions are governance functions, wherein: the    governance functions are configured to leverage at least one of the    governance classes.-   18D. The medium of embodiment 17D, wherein: a first subset of the    governance modelors are ontology governance modelors; and a second    subset of the governance modelors are taxonomy governance modelors.-   19D. The medium of embodiment 17D, wherein the formation    object-orientated orchestration further comprises: forming a first    training dataset from the datasets; training, with one or more    processors, a first machine-learning model on the first training    dataset by adjusting parameters of the first machine-learning model    to optimize a first objective function that indicates an accuracy of    the governance functions in complying with the governance    attributes; and storing, with one or more processors, the adjusted    parameters of the trained first machine-learning model in memory.-   20D. The medium of any one of embodiments 15D-19D, wherein: the    governance attributes comprise a plurality of access levels for    entity users of at least some of the plurality of entities; and the    specific targeted action comprises a plurality of sub-targets,    wherein: each of the plurality of sub-targets is assigned with a    subset of the plurality of access levels.-   21D. The medium of any one of embodiments 15D-20D, wherein the    plurality of object-manipulation functions comprise: a sequence    function used to change a collection of events into a time sequences    for processing; a feature function used to gather features of a    first object-orientation modelor and then use the features in a    second object-orientation modelor; an economic function used to:    gather economic objectives and economic constraints of an entity;    and employ an allocation algorithm to maximize the objectives; and    an ensembling function used to combine a first subset of the library    of classes.-   22D. The medium of embodiment 21D, wherein the plurality of    object-manipulation functions are arranged to change orders    dynamically based on the specific targeted action.-   23D. The medium of any one of embodiments 15D-22D, wherein the    plurality of object-orientation modelors comprise: ingestion    modelors used to control schema drift of the datasets and add    version numbers to the datasets; landing modelors used to clean    error records in the datasets and update the version numbers of the    datasets; curation modelors used to normalize the datasets, by    adding primary surrogate keys, and update the version numbers of the    datasets; dimensional modelors used to encode the datasets in    dimensional star schema and update the version numbers of the    datasets; and feature and label modelors used to: change the    datasets from dimensional star schema to denormalized flat table;    adjust granularity of the datasets; and update the version numbers    of the datasets.-   24D. The medium of any one of embodiments 15D-23D, wherein the    datasets comprise: training datasets, used to fit parameters of the    object-orientation modelors; validation datasets, used to tune the    parameters of the object-orientation modelors; quality assurance    datasets, used to test accuracy of the object-orientation modelors;    association datasets, used to relate datasets to each other; and    targeted action datasets, used to determine the set of actions    required to achieve the specific targeted action.-   25D. The medium of any one of embodiments 15D-24D, wherein: each    action from the set of actions is assigned with a score, the score    indicating impact level of the action in achieving the specific    targeted action.-   26D. The medium of any one of embodiments 15D-25D, wherein the    formation object-orientated orchestration further comprises: forming    a first training dataset from the datasets; training a first    machine-learning model on the first training dataset by adjusting    parameters of the first machine-learning model to optimize a first    objective function that indicates an accuracy of the plurality of    object-orientation modelors in complying with the governance    attributes; and storing the adjusted parameters of the trained first    machine-learning model in memory.-   1E. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors in a    computing system effectuate operations to execute quality management    of modelling methods for implementation of a machine learning design    in an object-oriented modeling (OOM) framework, the operations    comprising: writing, with the computing system, modelling-object    classes using object-oriented modelling of the modelling methods,    the modelling-object classes being members of a set of class    libraries; writing, with the computing system, quality-management    classes using object-oriented modelling of quality management, the    quality-management classes being members of the set of class    libraries; scanning, with the computing system, modelling-object    classes in the set of class libraries to determine modelling-object    class definition information; scanning, with the computing system,    quality-management classes in the set of class libraries to    determine quality-management class definition information; using,    with the computing system, the modelling-object class definition    information and the quality-management class definition information    to produce object manipulation functions that allow a quality    management system to access methods and attributes of    modelling-object classes to manipulate objects of the    modelling-object classes; and using, with the computing system, the    modelling-object class definition information and the    quality-management class definition information to produce access to    the object manipulation functions.-   2E. The medium of embodiment 1E, wherein: executing quality    management comprises executing a process that integrates raw data    ingestion, manipulation, transformation, composition, and storage    for building artificial intelligence models.-   3E. The medium of any one of embodiments 1E-2E, wherein: the modeled    quality management comprises management of extract, transform, and    load (ETL) phases of a machine learning model designed in the OOM    framework.-   4E. The medium of any one of embodiments 1E-3E, wherein: the modeled    quality management comprises reporting of model performance of a    machine learning model designed in the OOM framework.-   5E. The medium of embodiment 4E, wherein: model performance is    measured by recall, precision, or F1 score.-   6E. The medium of any one of embodiments 1E-5E, wherein: the modeled    quality management comprises data quality monitoring (DQM).-   7E. The medium of embodiment 6E, wherein: DQM comprises monitoring    data sources to detect a new or missing table or data element, data    element counts, data element null count and unique counts, or    datatype changes.-   8E. The medium of any one of embodiments 1E-7E, wherein: the modeled    quality management comprises model quality monitoring (MQM) of a    machine learning model designed in the OOM framework.-   9E. The medium of embodiment 8E, wherein: MQM comprises measuring a    model-based metric and causing model retraining responsive to    detecting more than a threshold amount of drift in the model-based    metric.-   10E. The medium of any one of embodiments 1E-9E, wherein: the    modeled quality management comprises score quality monitoring (SQM)    of a machine learning model designed in the OOM framework.-   11E. The medium of embodiment 10E, wherein: SQM comprises performing    a model hypothesis test.-   12E. The medium of embodiment 10E, wherein: SQM comprises computing    a lift table or a decile table.-   13E. The medium of any one of embodiments 1E-12E, wherein: the    modeled quality management comprises label quality monitoring (LQM)    of a machine learning model designed in the OOM framework.-   14E. The medium of embodiment 13E, wherein: LQM comprises    determining which data sources among a plurality of data sources are    more leverageable or impactful on model performance than other data    sources among the plurality of data sources.-   15E. The medium of any one of embodiments 1E-14E, wherein: the    modeled quality management comprises bias quality monitoring (BQM)    of a machine learning model designed in the OOM framework.-   16E. The medium of embodiment 15E, wherein BQM comprises detecting    information bias, selection bias, or confounding by the machine    learning model designed in the OOM framework.-   17E. The medium of any one of embodiments 1E-16E, wherein: the    modeled quality management comprises privacy quality monitoring    (PQM) of a machine learning model designed in the OOM framework.-   18E. The medium of any one of embodiments 1E-17E, wherein: the    modeled quality management comprises data quality monitoring (DQM)    of a machine learning model designed in the object-oriented modeling    (OOM) framework; DQM comprises monitoring data sources to detect a    new or missing table or data element, data element counts, data    element null count and unique counts, and datatype changes; the    modeled quality management comprises model quality monitoring (MQM)    of the machine learning model designed in the object-oriented    modeling (OOM) framework; MQM comprises measuring a model-based    metric and causing model retraining responsive to detecting more    than a threshold amount of drift in the model-based metric; the    model-based metric is indicative of an F1 score, accuracy,    precision, mean error, media error, distance measure, or recall; the    modeled quality management comprises score quality monitoring (SQM)    of the machine learning model designed in the object-oriented    modeling (OOM) framework; SQM comprises performing a model    hypothesis test and computing a lift table and a decile table based    on predicted probability of positive class membership, based on a    cumulative distribution function of positive cases; the model    hypothesis test comprises a Welch's t-test, Kolmogorov-Smirnov test,    or a Mann-Whitney U-test; the modeled quality management comprises    label quality monitoring (LQM) of the machine learning model    designed in the object-oriented modeling (OOM) framework; LQM    comprises determining which data sources among a plurality of data    sources are more leverageable or impactful on model performance than    other data sources among the plurality of data sources; the modeled    quality management comprises bias quality monitoring (BQM) of the    machine learning model designed in the object-oriented modeling    (OOM) framework; BQM comprises detecting information bias, selection    bias, and confounding by the machine learning model designed in the    object-oriented modeling (OOM) framework; the modeled quality    management comprises privacy quality monitoring (PQM) of the machine    learning model designed in the OOM framework.-   19E. The medium of any one of embodiments 1E-18E, wherein: the    modeled quality management comprises a process to determine data    source reliability.-   20E. The medium of any one of embodiments 1E-19E, wherein: an    attribute of a quality-management object in one of the    quality-management classes comprise means for characterizing quality    with the attribute of the quality-management object.-   21E. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors in a    computing system effectuate operations to measure the quality of    processing of data constructs in a pipeline using modelling methods    for implementation of machine learning design in an object-oriented    modeling (OOM) framework, the operations comprising: forming, with    one or more processors, a first plurality of classes using    object-oriented modelling of modelling methods; forming, with one or    more processors, a second plurality of classes using object-oriented    modelling of quality measurement methods; accessing, with one or    more processors, a class library containing at least some of the    first plurality of classes to determine first class definition    information of a modeling method class among the first plurality of    classes; accessing, with one or more processors, the class library    or another class library containing at least some of the second    plurality of classes to determine second class definition    information of a quality measurement class among the second    plurality of classes; and using, with one or more processors, the    first and the second class definition information to produce object    manipulation functions that allow a computing system to access    methods and attributes of data construct classes to manipulate data    construct objects.-   22E. The medium of embodiment 21E, the operations further    comprising: accessing the first and the second class definition    information to produce object manipulation functions that allow the    computing system to access methods and attributes of data construct    classes to manipulate data constructs; and accessing second class    definition information and third class definition information of the    data construct classes to produce object manipulation functions that    allow the computing system to access methods and attributes of data    construct classes to manipulate said data constructs.-   23E. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by one or more processors in a    computing system effectuate operations to measure the quality of    processing of data constructs in a pipeline using modelling methods    for implementation of machine learning design in an object-oriented    modeling (OOM) framework, the operations comprising: writing, with    the computing system, a first plurality of classes using    object-oriented modelling of modelling methods; writing, with the    computing system, a second plurality of classes using    object-oriented modelling of quality measurement methods; scanning,    with the computing system, a class library set containing a    modelling method class among the first plurality of classes to    determine first class definition information; scanning, with the    computing system, a class library set containing a quality    management class among the second plurality of classes to determine    second class definition information; and invoking, with the    computing system, the first class definition information of the    quality management class and the second class definition information    of the modeling method class to produce object manipulation    functions that allow the computing system to access the methods and    attributes of data classes to manipulate the modeling method class.-   24E. The medium of embodiment 23E, wherein the object manipulation    functions comprise operations of: adding an attribute to an object,    deleting an attribute of an object, updating an attribute to an    object, reading an attribute of an object, adding a reference to an    object as an attribute, changing an order of attributes, using a    setter, or using a getter.-   25E. The medium of embodiment 23E, wherein the object manipulation    functions comprise operations of: formatting an attribute,    aggregating an attribute, calculating an attribute, semantically    altering attributes, aggregating an attribute, contracting    attribute, or expanding an attribute.-   26E. The medium of embodiment 23E, wherein the object manipulation    functions comprise operations of: adding an attribute to an object,    deleting an attribute of an object, updating an attribute to an    object, reading an attribute of an object, adding a reference to an    object as an attribute, changing an order of attributes, using a    setter, using a getter; formatting an attribute, aggregating an    attribute, calculating an attribute, semantically altering    attributes, aggregating an attribute, contracting attribute, and    expanding an attribute.-   27E. The medium of embodiment 23E, wherein object manipulation is    conditional.-   28E. The medium of embodiment 23E, wherein the quality measurement    methods comprise: data quality, model quality, score quality, bias    quality, and label quality.-   29E. The medium of embodiment 23E, wherein: entities captured in    data construct objects processed by the computing system include    consumers; communications to consumers by an enterprise;    communications to an enterprise by consumers; the events include    purchases by consumers from the enterprise; the events include    non-purchase interactions by consumers with the enterprise; and the    entity logs are obtained from a customer relationship management    system of the enterprise.-   30E. The medium of embodiment 23E, wherein: the enterprise is a    credit card issuer and a trained predictive machine learning models    developed using the object-oriented modeling (OOM) framework is    configured to predict whether a consumer will default; the    enterprise is a lender and the trained predictive machine learning    model developed using the OOM framework is configured to predict    whether a consumer will borrow; the enterprise is an insurance    company and the trained predictive machine learning model developed    using the OOM framework is configured to predict whether a consumer    will file a claim; the enterprise is an insurance company and the    trained predictive machine learning model developed using the OOM    framework is configured to predict whether a consumer will sign-up    for insurance; the enterprise is a vehicle seller and the trained    predictive machine learning model developed using the OOM framework    is configured to predict whether a consumer will purchase a vehicle;    the enterprise is a seller of goods and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer will file a warranty claim,    or the enterprise is a wireless operator and the trained predictive    machine learning model developed using the OOM framework is    configured to predict whether a consumer upgrade their cellphone, or    the enterprise is bank and the trained predictive machine learning    model developed using the OOM framework is configured to predict the    change in GDP.-   1F. A method, comprising: the operations of any one of embodiments    1A-30E.-   2F. A system, comprising: the media of any one of embodiments 1A-30    coupled to one or more processors configured to execute the    instructions stored on the media.

What is claimed is:
 1. A tangible, non-transitory, machine-readablemedium storing instructions that when executed by one or more processorseffectuate operations comprising: writing, with a computing system, afirst plurality of classes using object-oriented modelling of modellingmethods; writing, with the computing system, a second plurality ofclasses using object-oriented modelling of governance; scanning, withthe computing system, a set of libraries collectively containing bothmodelling object classes among the first plurality of classes andgovernance classes among the second plurality of classes to determineclass definition information; using, with the computing system, at leastsome of the class definition information to produce object manipulationfunctions, wherein the object manipulation functions allow a governancesystem to access methods and attributes of classes among the firstplurality of classes or the second plurality of classes to manipulateobjects of at least some of the modelling object classes; and using atleast some of the class definition information to effectuate access tothe object manipulation functions.
 2. The medium of claim 1, wherein:the operations execute quality management of modelling methods forimplementation of machine learning design in an object-oriented modeling(OOM) framework.
 3. The medium of claim 1, wherein: the modeledgovernance comprises a set of structures, processes, or policies bywhich pipeline development, deployment, or use functionality within anorganization or set of organizations is directed, managed, orcontrolled.
 4. The medium of claim 1, wherein: the modeled governancecomprises a set of structures, processes, and policies by which pipelinedevelopment, deployment, and use functionality within an organization orset of organizations is directed, managed, and controlled.
 5. The mediumof claim 1, wherein: the modeled governance comprises a policy; and thepolicy comprises a set of rules, controls, or resolutions put in placeto dictate model behavior.
 6. The medium of claim 1, wherein: themodeled governance comprises a policy; and the policy comprises a set ofrules, controls, or resolutions put in place to dictate modelversioning.
 7. The medium of claim 1, wherein: the modeled governancecomprises a set of policies; and meta-policies having detection rulesare used to detect conflicts among the policies.
 8. A tangible,non-transitory, machine-readable medium storing instructions that whenexecuted by one or more processors in a computing system effectuateoperations to manage compliance governance using modelling methods in apipeline for implementation of machine learning design in anobject-oriented modeling (OOM) framework, the operations comprising:forming, with the one or more processors, a first plurality of classesusing object-oriented modelling of the modelling methods; forming, withthe one or more processors, a second plurality of classes usingobject-oriented modelling of governance compliance methods; scanning,with the one or more processors, a class library containing modellingmethod classes to determine a first part of class definitioninformation; scanning, with the one or more processors, a class librarycontaining governance compliance classes to determine a second part ofclass definition information; and using, with the one or moreprocessors, the first part of class definition information of themodeling method class and the second part of class definitioninformation of the governance compliance classes to produce objectmanipulation functions that allow the computing system to access methodsand attributes of a governance compliance object to manipulate a methodclass object.
 9. A tangible, non-transitory, machine-readable mediumstoring instructions that when executed by one or more processorseffectuate operations comprising: obtaining, with the one or moreprocessors, for a plurality of entities, datasets, wherein: the datasetscomprise a plurality of entity logs; a first subset of the plurality ofentity logs are events involving the entities; a first subset of theevents are actions by the entities; at least some of the actions aretargeted actions; a second subset of the plurality of entity logs areattributes related to the entities; a first subset of the attributes aregovernance attributes, wherein the governance attributes comprise:entity restrictions for at least some of the plurality of entities;entity business protocols for at least some of the plurality ofentities: entity policies for at least some of the plurality ofentities; entity authorized users for at least some of the plurality ofentities; and entity security protocols for at least some of theplurality of entities; and the events are distinct from the attributes;forming, with the one or more processors, an object-orientatedorchestration, the object-orientated orchestration comprising: forming aplurality of objects, wherein each object of the plurality of objectscomprises a different set of attributes and events; formingobject-oriented labeled datasets based on the event and the attributesof each of the datasets; forming a library of classes, generated by aplurality of object-orientation modelors; and forming a plurality ofobject-manipulation functions, each function configured to leverage aspecific class; receiving a request to determine a set of actionsrequired to achieve a specific targeted action; assigning the specifictargeted action to a first subset of classes from the library of classesof the object-oriented orchestration; and determining, with the one ormore processors, the set of actions required to achieve the specifictargeted action using a first subset of the plurality ofobject-manipulation functions related to the first subset of classesfrom the library of classes of the object-oriented orchestration. 10.The medium of claim 9, wherein: a first subset of the plurality ofobject-orientation modelors are governance modelors; the governancemodelors are configured to form governance classes; and a second subsetof the plurality of object-manipulation functions are governancefunctions, wherein: the governance functions are configured to leverageat least one of the governance classes.
 11. The medium of claim 10,wherein: a first subset of the governance modelors are ontologygovernance modelors; and a second subset of the governance modelors aretaxonomy governance modelors.
 12. The medium of claim 9, wherein: thegovernance attributes comprise a plurality of access levels for entityusers of at least some of the plurality of entities; and the specifictargeted action comprises a plurality of sub-targets, wherein: each ofthe plurality of sub-targets is assigned with a subset of the pluralityof access levels.
 13. The medium of claim 9, wherein the plurality ofobject-manipulation functions comprise: a sequence function used tochange a collection of events into a time sequences for processing; afeature function used to gather features of a first object-orientationmodelor and then use the features in a second object-orientationmodelor; an economic function used to: gather economic objectives andeconomic constraints of an entity; and employ an allocation algorithm tomaximize the objectives; and an ensembling function used to combine afirst subset of the library of classes.
 14. The medium of claim 13,wherein the plurality of object-manipulation functions are arranged tochange orders dynamically based on the specific targeted action.
 15. Themedium of claim 9, wherein the plurality of object-orientation modelorscomprise: ingestion modelors used to control schema drift of thedatasets and add version numbers to the datasets; landing modelors usedto clean error records in the datasets and update the version numbers ofthe datasets; curation modelors used to normalize the datasets, byadding primary surrogate keys, and update the version numbers of thedatasets; dimensional modelors used to encode the datasets indimensional star schema and update the version numbers of the datasets;and feature and label modelors used to: change the datasets fromdimensional star schema to denormalized flat table; adjust granularityof the datasets; and update the version numbers of the datasets.
 16. Themedium of claim 9, wherein the datasets comprise: training datasets,used to fit parameters of the object-orientation modelors; validationdatasets, used to tune the parameters of the object-orientationmodelors; quality assurance datasets, used to test accuracy of theobject-orientation modelors; association datasets, used to relatedatasets to each other; and targeted action datasets, used to determinethe set of actions required to achieve the specific targeted action. 17.The medium of claim 9, wherein: each action from the set of actions isassigned with a score, the score indicating impact level of the actionin achieving the specific targeted action.
 18. The medium of claim 10,wherein the object-orientated orchestration further comprises: forming afirst training dataset from the datasets; training, with the one or moreprocessors, a first machine-learning model on the first training datasetby adjusting parameters of the first machine-learning model to optimizea first objective function that indicates an accuracy of the governancefunctions in complying with the governance attributes; and storing, withthe one or more processors, the adjusted parameters of the trained firstmachine-learning model in memory.
 19. The medium of claim 9, wherein theobject-orientated orchestration further comprises: forming a firsttraining dataset from the datasets; training a first machine-learningmodel on the first training dataset by adjusting parameters of the firstmachine-learning model to optimize a first objective function thatindicates an accuracy of the plurality of object-orientation modelors incomplying with the governance attributes; and storing the adjustedparameters of the trained first machine-learning model in memory.